Matches in Nanopublications for { ?s <http://www.w3.org/2000/01/rdf-schema#comment> ?o ?g. }
- func-network comment "Interactive graph display of sRNA-gene interactions" assertion.
- func-table comment "Searchable table of interactions with metadata" assertion.
- func-highlight comment "Direct highlighting in PubMed Central articles" assertion.
- func-sparql comment "User-friendly SPARQL query interface" assertion.
- etl-process comment "Extract-Transform-Load pipeline from GFF files to Wikidata" assertion.
- etl-extract comment "Load GFF files from NCBI RefSeq" assertion.
- etl-transform comment "Convert GFF records to Wikidata item definitions" assertion.
- etl-load comment "Upload items to Wikidata via API" assertion.
- interactions comment "Regulatory relationships modeled" assertion.
- citation-system comment "Wikidata properties linking interactions to source articles and quotations" assertion.
- property-p1683 comment "Exact textual excerpt from source" assertion.
- property-p248 comment "Links claim to source article" assertion.
- property-p932 comment "PubMed Central identifier for open access" assertion.
- srna-omra comment "Outer membrane stress-response small RNA" assertion.
- gene-csgf comment "Curli major subunit gene" assertion.
- benefit-quality comment "Community curation at statement level" assertion.
- benefit-reproducibility comment "Researchers can verify claims directly from source" assertion.
- deployment-toolforge comment "Hosting platform for Wikidata applications" assertion.
- sparql-arch comment "Dynamic query generation system from user interface inputs" assertion.
- web-interface comment "Frontend for querying sRNA regulatory networks" assertion.
- web-interface comment "The InteractOA web interface uses Python and Flask technologies" assertion.
- web-interface comment "The web interface makes InteractOA compliant with FAIR principles" assertion.
- wikidata-endpoint comment "Official SPARQL endpoint for Wikidata" assertion.
- python comment "General-purpose programming language used for InteractOA backend" assertion.
- flask comment "Lightweight Python web framework for building web applications" assertion.
- flask comment "Flask is built on top of Python" assertion.
- d3js comment "JavaScript library for interactive data visualizations" assertion.
- d3js comment "D3.js is the underlying technology for network visualization" assertion.
- wikidataintegrator comment "WikidataIntegrator is built on Python" assertion.
- wikidataintegrator comment "Python library for programmatic Wikidata queries and data integration" assertion.
- wikidataintegrator comment "WikidataIntegrator serves as middleware between Flask backend and Wikidata SPARQL endpoint" assertion.
- component-network comment "Interactive D3.js network graph for sRNA-target interactions" assertion.
- component-network comment "Interactive D3.js network graph showing sRNA-target interactions" assertion.
- component-filters comment "Customizable query parameters for network filtering" assertion.
- component-highlight comment "Direct highlighting of sRNA mentions in PubMed Central articles" assertion.
- component-table comment "Table entries link to highlighted articles" assertion.
- component-table comment "Searchable and filterable table of sRNA-target interactions" assertion.
- query-step-1 comment "Pre-defined SPARQL query template" assertion.
- query-step-1 comment "Template definition is the first step in the query pipeline" assertion.
- query-step-2 comment "Map user selections to query variables" assertion.
- query-step-3 comment "Assemble complete SPARQL from template" assertion.
- query-step-3 comment "Query composition depends on both template and bindings" assertion.
- query-step-4 comment "Query execution follows query composition" assertion.
- query-step-4 comment "Submit to Wikidata Query Service" assertion.
- query-step-5 comment "Enrich results with external links" assertion.
- query-step-5 comment "Result processing depends on query execution" assertion.
- query-step-6 comment "Display in network or table format" assertion.
- query-step-6 comment "Rendering uses processed results" assertion.
- benefit-community comment "Wikimedia Foundation ensures long-term maintenance" assertion.
- benefit-corpus comment "Millions of items for linking and integration" assertion.
- benefit-interface comment "Intuitive editing enables community contribution" assertion.
- benefit-license comment "Unrestricted data reuse" assertion.
- benefit-sparql comment "Supports complex federated queries" assertion.
- benefit-toolforge comment "Sustainable long-term deployment infrastructure" assertion.
- wikidata-benefits comment "Six key advantages of using Wikidata as foundation" assertion.
- fair-compliance comment "InteractOA conforms to FAIR principles: Findable, Accessible, Interoperable, Reusable" assertion.
- sustain-backing comment "Wikimedia Foundation support" assertion.
- sustain-curation comment "Distributed volunteer effort prevents single points of failure" assertion.
- sustain-dumps comment "Complete snapshots for independent preservation" assertion.
- sustain-versioning comment "Full edit history and revert capability" assertion.
- sustainability comment "Solutions to data decay and project termination" assertion.
- interop-federated comment "Query multiple RDF endpoints simultaneously" assertion.
- interop-linked comment "RDF enables linking to external ontologies" assertion.
- interop-linking comment "Universal identifiers connect diverse data types" assertion.
- interoperability comment "Integration with broader research ecosystem" assertion.
- comk_mutant_reduced_survival_mouse comment "Comparative evidence: demonstrates role of ComK in infection survival" assertion.
- comk_mutant_impaired_survival_galleria comment "Cross-model evidence: consistent with mouse model results" assertion.
- ACTRIS-DMP comment "The goal of the ACTRIS Data Management Plan is to outline the strategy and process towards making ACTRIS data FAIR at ACTRIS Data Centre Level." assertion.
- ACTRIS-DMP comment "The goal of the ACTRIS Data Management Plan is to outline the strategy and process towards making ACTRIS data FAIR at ACTRIS Data Centre Level." assertion.
- arXiv.2504.03866 comment "This summarizes the three main application areas identified by the authors: statistical methods (regression, Monte Carlo, dimensionality reduction), network analysis (community detection, flow optimization, phylogenetics), and dynamical systems modeling (ODEs/PDEs for population dynamics). Directly relevant to the systematic review's scope on quantum computing applications for biodiversity research." assertion.
- arXiv.2504.03866 comment "Illustrates the potential magnitude of quantum advantage for combinatorial optimization problems common in ecology. This exponential speedup scenario represents the theoretical upper bound of benefits, though practical realization depends on fault-tolerant quantum computing which is not yet available. Important context for setting realistic expectations in biodiversity applications." assertion.
- arXiv.2504.03866 comment "Critical limitation for practical applications - ecological and biodiversity data is inherently classical (species counts, GPS coordinates, environmental variables) and encoding it for quantum computation is non-trivial. This state preparation bottleneck is an essential caveat that may limit near-term practical benefits for biodiversity research." assertion.
- arXiv.2504.03866 comment "Important context about current hardware limitations. NISQ (Noisy Intermediate-Scale Quantum) devices are what researchers have access to today. Most ecological and biodiversity applications would need to use NISQ-compatible algorithms like QAOA and VQE in the near term, with more powerful fault-tolerant algorithms remaining a future prospect." assertion.
- DANS_DataStationPolicy comment "The scope of the DANS Data Stations Policy (“the Policy”) is limited to datasets for which DANS performs ingestion, curation, archiving, long-term preservation, and dissemination. Such datasets are stored and managed in the repository services referred to as a ‘DANS Data Station’, which combines a public facing, domain specific repository based on Dataverse software where the datasets are managed and made accessible, and a service referred to as ‘the Vault’ where datasets are archived for long-term preservation. The Policy does not consider archiving and preservation of other materials, such as DANS’s web pages, internal and external documents, and digital objects in any other services that DANS provides. The Policy governs the obligations, responsibilities, and expectations of the role players." assertion.
- s10900-024-01390-1 comment "Although climate change is increasing wildfire and smoke events globally, public health messaging and individual access to resources for protection are limited. Individual interventions can be highly effective at reducing wildfire smoke exposure. However, studies related to individual responses to wildfire smoke are limited and demonstrate mixed protective behaviors and risk perception. Our research helps fill this gap by assessing the self-reported behavior of 20 participants during wildfire season in Western Montana from 28 June through 1 November, 2022. We also measured continuous outdoor and indoor fine particulate matter (PM2.5) concentrations at participant residencies during this time period using PurpleAir sensors (PAII-SD, PurpleAir, Inc, USA) while participants took up to 16 self-reported online weekly activity surveys. Mixed-effect Poisson regression models were used to assess associations between exposure variables and participant reported behaviors. These results were compared with end-of-study interview findings. Wildfire smoke impacted days and increased concentrations of PM2.5 were associated with decreased outdoor exercise and opening of windows for ventilation. Interview themes were congruent with the regression analysis, with the additional finding of high portable air cleaner (PAC) use among participants. Additionally, these interviews gave context to both the tradeoffs participants face when making protective decisions and the importance of personal air quality data in increasing awareness about wildfire smoke risks. Future wildfire smoke studies can build off this research by providing personally relevant air quality data and PACs to participants and by improving public health messaging to address the compounding risks of wildfire smoke exposure and heat. Findings of this article are that residents in Western Montana respond to wildfire smoke by reducing outdoor exercise by 22% to 23% and window ventilation by 22% to 29%. Data from a group of participants who were 100% White and 85% female shows that 70% utilized portable air cleaners, a rate significantly higher than the 29% observed in broader national studies. While high education levels and access to personal air quality data drive these protective behaviors, residents without air conditioning face critical trade-offs between limiting smoke infiltration and managing extreme heat." assertion.
- articles%3A42499 comment "Large-scale atmospheric field campaigns conducted over the last half-century have fundamentally transformed the understanding of how vegetation fires influence global air quality and climate. From early studies of fire behavior to recent, sophisticated missions like WE-CAN and FIREX-AQ, research has successfully identified key chemical transformations in smoke plumes, including the evolution of nitrogen and the mechanisms of daytime ozone formation. Despite these advances, significant gaps remain regarding the effects of weather and fuel characteristics on emissions. Future research must prioritize understudied regions such as tropical peatlands and sub-Saharan Africa, integrate emerging technologies like unmanned aerial vehicles (UAVs) and machine learning, and address the specific health risks associated with burning non-vegetative fuels in the wildland-urban interface. Major findings:Research has evolved to show how smoke chemically changes ozone and climate, but better ground-based data is still needed to supplement satellite coverage gaps. Future efforts must focus on under-studied regions like Africa and the unique health risks created when non-vegetative materials burn in residential areas." assertion.
- 10947128 comment "Abstract: In remote sensing image processing for Earth and environmental applications, super-resolution (SR) is a crucial technique for enhancing the resolution of low-resolution (LR) images. In this study, we proposed a novel algorithm of frequency-domain super-resolution with reconstruction from compressed representation. The algorithm follows a multistep procedure: first, an LR image in the space domain is transformed to the frequency domain using a Fourier transform. The frequency-domain representation is then expanded to the desired size (number of pixels) of a high-resolution (HR) image. This expanded frequency-domain image is subsequently inverse Fourier transformed back to the spatial domain, yielding an initial HR image. A final HR image is then reconstructed from the initial HR image using a low-rank regularization model that incorporates a nonlocal smoothed rank function (SRF). We evaluated the performance of the new algorithm by comparing the reconstructed HR images with those generated by several commonly used SR algorithms, including: 1) bicubic interpolation; 2) sparse representation; 3) adaptive sparse domain selection and adaptive regularization; 4) fuzzy-rule-based (FRB) algorithm; 5) SR convolutional neural networks (SRCNNs); 6) fast SR convolutional neural networks (FSRCNNs); 7) practical degradation model for deep blind image SR; 8) the frequency separation for real-world SR (FSSR); and 9) the enhanced SR generative adversarial networks (ESRGANs). The algorithms were tested on Landsat-8 and Moderate Resolution Imaging Spectroradiometer (MODIS) multiresolution images over various locations, as well as on images with artificially added noise to assess the robustness of each algorithm. Results show that: 1) the proposed new algorithm outperforms the others in terms of the peak signal-to-noise ratio, structure similarity, and root-mean-square error and 2) it effectively suppresses noise during HR reconstruction from noisy low-resolution (LR) images, overcoming a key limitation of existing SR methods." assertion.
- s10460-024-10582-3 comment "Climate change is expected to increase the frequency and intensity of drought in many parts of the world, including Montana. In the face of worsening drought conditions, agricultural producers need to adapt their operations to mitigate risk. This study examined the role of local knowledge and climate information in drought-related decisions through five focus groups with Montana farmers and ranchers. We found that trust and risk perceptions mediated how producers utilized both local knowledge and climate information. More specifically, producers relied on local knowledge in drought-related decisions, regarding their own observation and past experience as trustworthy and not particularly risky. In contrast, climate information and seasonal climate forecasts in particular were regarded as risky and untrustworthy, largely due to a perceived lack of accuracy. Since producers tended to be risk averse, especially given market and climate uncertainties, they rarely relied on “risky” climate information. At the same time, producers actively managed risk and tested out new technologies and practices through processes of trial and error, what they called “experimenting,” which enabled them to build firsthand knowledge of potential adaptations. In the context of uncertainty and risk aversion, programs that reduce the financial risk of experimenting with new technologies and adaptive practices are needed to enable producers to develop direct experience with innovations designed to mitigate drought risk. Further, scientists developing climate information need to work directly with farmers and ranchers to better integrate local knowledge into climate information. Major findings: Montana agricultural producers prioritize local experiential knowledge over seasonal climate forecasts because they frequently view science-driven data as inaccurate, untrustworthy, or poorly scaled to their specific lands. Due to extreme financial vulnerability, these producers are highly risk-averse and often perceive unproven climate information as an added danger to their livelihoods rather than a tool for mitigation. To manage this uncertainty, they utilize small-scale on-farm "experiments" to build trusted local knowledge before adopting new practices or technologies on a larger scale. The study concludes that successful drought adaptation requires a "farmer first" approach where scientists co-produce climate services that integrate local observations and provide financial support for producer-led experimentation." assertion.
- S0921800924001824 comment "A significant cost of wildfires is the exposure of local and regional populations to air pollution from smoke, which can travel hundreds of miles from the source fire and is associated with significant negative health consequences. Wildfires are increasing in frequency and intensity in the United States, driven by historic fire management approaches and global climate change. These influences will take many decades or longer to reverse, so the main opportunities for mitigating health effects involve minimizing human exposure through changes in behavior or infrastructure. One key recommendation for reducing pollution exposures during wildfire smoke events is to limit time and physical activity outdoors, but there is limited evidence on the extent to which people make this change. We estimate how use of parks and playgrounds changes with air quality during wildfire season in the northwest United States. We find small reductions in park and playground visits on moderately polluted days, and large reductions, to 50–60% of baseline visits, when pollution levels are high. Disaggregating results by neighborhood characteristics, we find a significantly greater behavioral response to moderate levels of air pollution in neighborhoods with higher socio-economic status, although responses to high levels of pollution are similar across neighborhood types. Major findings: Park and playground visitation in the Northwest United States decreases significantly as wildfire-driven air quality worsens, with visits dropping by up to 50% during hazardous conditions. A key disparity emerged showing that residents of higher-income and more educated neighborhoods begin taking protective action at much lower pollution levels than those in less advantaged areas, who typically only reduce activity when air quality reaches severe levels. These findings suggest that socioeconomic differences in health outcomes from wildfire smoke are driven partly by the varying ability of different groups to bear the costs of forgoing outdoor recreation." assertion.
- 241 comment "settingsOrder Article Reprints Open AccessReview Prescribed Fire Smoke: A Review of Composition, Measurement Methods, and Analysis by Kayode I. Fesomade 1,2ORCID andRobert A. Walker 1,2,*ORCID 1 Montana Materials Science Program, Montana State University, Bozeman, MT 59717, USA 2 Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT 59717, USA * Author to whom correspondence should be addressed. Fire 2025, 8(7), 241; https://doi.org/10.3390/fire8070241 Submission received: 30 April 2025 / Revised: 16 June 2025 / Accepted: 17 June 2025 / Published: 20 June 2025 (This article belongs to the Section Fire Science Models, Remote Sensing, and Data) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract Prescribed fire has become an increasingly important strategy for removing biomass from forests and mitigating the risk of severe wildfire. When considering where and to what extent prescribed fire should be applied, land resource managers must consider a host of concerns including biomass density, moisture content, and meteorological conditions. These variables will not only affect how effective the burn will be, but also what sort of smoke is produced by the prescribed fire and how that smoke impacts individuals and local communities. After briefly summarizing how prescribed fire practices have evolved, this review describes how the properties of prescribed fire smoke depend on prescribed fire conditions and the methods used to measure molecular and particulate species in prescribed fire smoke. The closing section of this review identifies areas where advances in smoke monitoring and characterization can continue to improve our understanding of prescribed fire behavior. Keywords: prescribed fire; wildfire; smoke; particulate matter; emission factor; measurement; modified combustion efficiency; carbon budget. Major findings: Prescribed fire is a vital strategy for managing forests and reducing wildfire danger by clearing away excess plants through low-intensity, controlled burns that produce significantly less harmful smoke than major wildfires. This review highlights how using high-tech monitoring tools to track specific chemicals like CO2 and particulate matter helps experts protect community air quality while making local ecosystems more resilient to future disasters." assertion.
- 2024GL109369 comment "Wildfires have torn across western North America over the last decade. Smoke from wildland fires in Canada can travel thousands of kilometers to US cities and reacts with urban pollution to create harmful ozone, a criteria pollutant regulated by the US Environmental Protection Agency. Accurately quantifying this impact is needed to inform US air quality policy, but is challenging due to complex physical and chemical processes. In this study, we analyze surface and airborne measurements, alongside a new variable-resolution global chemistry-climate model, to better understand these processes. We show that the near-field conversion of nitrogen oxide (NOx) emissions from wildfires to peroxyacetyl nitrate (PAN) and other more oxidized forms reduces their localized impacts on ozone. PAN is the principal tropospheric reservoir for NOx radicals. When aged smoke plumes descend southward from Canada toward US cities, higher temperatures cause PAN to decompose and thus help production of ozone during smoke transport. On days when the observed ozone levels exceed the air quality limit (70 ppbv for 8-hr average), wildfire smoke can contribute 5–25 ppbv. Major Findings: Research using the AM4VR model demonstrates that wildfire nitrogen emissions are rapidly sequestered as peroxyacyl nitrates ($PAN$), which remain stable during long-range transport before decomposing to release NO and fuel ozone O3 production in distant urban areas. These findings reveal that such chemical evolution can increase surface O3 levels by 5 to 25 ppbv in cities thousands of kilometers downwind, particularly when pyrogenic organic compounds interact with existing urban pollution." assertion.
- 1.JRS.18.024513.full comment "The increasing prevalence of nuisance benthic algal blooms in freshwater systems has led to water quality monitoring programs based on the presence and abundance of algae. Large blooms of the nuisance filamentous algae, Cladophora glomerata, have become common in the waters of the Upper Clark Fork River in western Montana. To aid in the understanding of algal growth dynamics, unoccupied aerial vehicle (UAV)-based hyperspectral images were gathered at three field sites along the length of the river throughout the growing season of 2021. Select regions within images covering the spectral range of 400 to 850 nm were labeled based on a combination of professional judgment and spectral profiles and used to train a random forest classifier to identify benthic algal growth across several classes, including benthic growth dominated by Cladophora (Clado), benthic growth dominated by growth forms other than Cladophora (non-Clado), and areas below a visually detectable threshold of benthic growth (bare substrate). After classification, images were stitched together to produce spatial distribution maps of each river reach while also calculating the average percent cover for each reach, achieving an accuracy of approximately 99% relative to manually labeled images. Results of this analysis showed strong variability across each reach, both temporally (up to 40%) and spatially (up to 46%), indicating that UAV-based imaging with high-spatial resolution could augment and therefore improve traditional measurement techniques that are spatially limited, such as spot sampling. Major findings: The study successfully utilized UAV-based hyperspectral imaging and a random forest classification model to map benthic algae in the Upper Clark Fork River with 99% accuracy, effectively distinguishing Cladophora from other growth forms and bare substrate. The researchers discovered significant spatial and temporal variability in algal coverage, with levels fluctuating by over 40% within short river reaches, demonstrating that high-resolution remote sensing provides a more comprehensive and accurate assessment of ecosystem health than traditional spot-sampling." assertion.
- 3626772.3657668 comment "Major findings: ScholarNodes, a web interface that successfully integrates partition-based (Louvain) and similarity-based (Spectral) community detection algorithms with the BM25 ranking algorithm to recommend interdisciplinary collaborations within academic social networks. By analyzing publication metadata from the OpenAlex dataset, the authors demonstrated that a topic-similarity network can effectively identify latent researcher communities and provide meaningful mentorship and grant-teaming recommendations that transcend traditional departmental boundaries." assertion.
- pan3.10695 comment "Social connections among individuals are essential components of social-ecological systems (SESs), enabling people to take actions to more effectively adapt or transform in response to widespread social-ecological change. Although scholars have associated social connections and cognitions with adaptive capacity, measuring actors' social networks may further clarify pathways for bolstering resilience-enhancing actions. We asked how social networks and socio-cognitions, as components of adaptive capacity, and SES regime shift severity affect individual landscape management behaviours using a quantitative analysis of ego network survey data from livestock producers and landcover data on regime shift severity (i.e. juniper encroachment) in the North American Great Plains. Producers who experienced severe regime shifts or perceived high risks from such shifts were not more likely to engage in transformative behaviour like prescribed burning. Instead, we found that social network characteristics explained significant variance in transformative behaviours. Policy implications: Our results indicate that social networks enable behaviours that have the potential to transform SESs, suggesting possible leverage points for enabling capacity and coordination toward sustainability. Particularly where private lands dominate and cultural practices condition regime shifts, clarifying how social connections promote resilience may provide much needed insight to bolster adaptive capacities in the face of global change. Major findings: This research explores how social networks and individual beliefs influence land management behaviors among livestock producers in the North American Great Plains. The study found that social networks are significantly more effective at predicting "transformative" behaviors—such as prescribed burning—than the actual ecological condition of the land. While "adaptive" behaviors like mechanical tree removal are often triggered by observing physical changes in the environment (regime shifts), major transformative actions depend heavily on the social support, information access, and trust provided by an individual's community and professional connections. These findings highlight that social constraints often limit environmental management more than a lack of information about ecological risks." assertion.
- 2024JD041640 comment "Salt Lake City, Utah has higher concentrations of ozone, a pollutant harmful to human and plant life, in the atmosphere than the standard set by the United States Environmental Protection Agency (US EPA). The reasons for the high levels of ozone remain uncertain. Volatile organic compounds (VOCs) are a class of air pollutants that undergo reactions that produce ozone. Understanding their sources and reactions is important to be able to reduce air pollution. In this study, we measured 35 VOCs in SLC in August and September 2022 and used a model to identify their major sources. Concentrations of hazardous VOCs identified by the US EPA increased by 45%–217% when wildfire smoke was present in the air. Methanol and ethanol were the most important VOCs in terms of total concentration in the air, while isoprene and monoterpenes were the most important in terms of reactions that could create ozone. According to the model results, VOCs are emitted from five major sources including traffic and solvent use. Further measurements are needed to confirm the model results and reduce uncertainty of the important sources of VOCS. Major findings:The Salt Lake regional Smoke, Ozone and Aerosol Study (SAMOZA) conducted in late 2022 measured 35 different volatile organic compounds (VOCs) using advanced mass spectrometry and liquid chromatography. The researchers found that total VOC levels in the city averaged 32 parts per billion (ppb), but spiked as high as 141 ppb during certain hours. This data is used to calculate "OH reactivity," which helps scientists understand how quickly these chemicals react in the air to form pollutants like smog and ozone." assertion.
- cobi.14243 comment "Wildlife conservation depends on supportive social as well as biophysical conditions. Social identities such as hunter and nonhunter are often associated with different attitudes toward wildlife. However, it is unknown whether dynamics within and among these identity groups explain how attitudes form and why they differ. To investigate how social identities help shape wildlife-related attitudes and the implications for wildlife policy and conservation, we built a structural equation model with survey data from Montana (USA) residents (n = 1758) that tested how social identities affect the relationship between experiences with grizzly bears (Ursus arctos horribilis) and attitudes toward the species. Model results (r2 = 0.51) demonstrated that the hunter identity magnified the negative effect of vicarious property damage on attitudes toward grizzly bears (β = −0.381, 95% confidence interval [CI]: −0.584 to −0.178, p < 0.001), which in turn strongly influenced acceptance (β = −0.571, 95% CI: −0.611 to −0.531, p < 0.001). Our findings suggested that hunters’ attitudes toward grizzly bears likely become more negative primarily because of in-group social interactions about negative experiences, and similar group dynamics may lead nonhunters to disregard the negative experiences that out-group members have with grizzly bears. Given the profound influence of social identity on human cognitions and behaviors in myriad contexts, the patterns we observed are likely important in a variety of wildlife conservation situations. To foster positive conservation outcomes and minimize polarization, management strategies should account for these identity-driven perceptions while prioritizing conflict prevention and promoting positive wildlife narratives within and among identity groups. This study illustrates the utility of social identity theory for explaining and influencing human–wildlife interactions. Major findings: This study examined how belonging to a social group—specifically identifying as a "hunter" or "nonhunter"—affects how people in Montana feel about grizzly bears. The researchers found that hunters' attitudes become much more negative when they hear stories of property damage from other hunters (their "in-group"), because people naturally trust and empathize more with members of their own group. Conversely, nonhunters are less influenced by these negative stories but tend to develop more positive feelings after having neutral or peaceful experiences with bears. The findings suggest that to protect wildlife effectively, managers must understand these social identities and work to prevent groups from becoming divided or angry with each other." assertion.
- 10871209.2024.2318330?needAccess=true comment "Despite years of research, concepts such as human tolerance andacceptability of wildlife remain inconsistently defined and measured,creating confusion, undermining comparative and longitudinalresearch, and limiting utility to practitioners. To address these short-comings, the wildlife attitude-acceptability framework proposed inter-secting attitudes toward wildlife species with acceptability of impactsfrom that species to reveal four archetypes of human cognitionstoward wildlife. Here, we use data from western US household surveysto populate the conceptual space of the wildlife attitude-acceptabilityframework with human cognitions toward three carnivore species:gray wolf (Canis lupis), cougar (Puma concolor), and grizzly bear(Ursus arctos horribilis). This empirical application of the wildlife atti-tude-acceptability framework demonstrates its potential to informmanagement and conservation efforts, promote consistent measure-ment across species and studies, and extend theoretical understand-ing of concepts like tolerance, which are necessary for human–wildlifecoexistence. We discuss these opportunities and remaining needs forimprovement before wider adoption.KEYWORDSCarnivores; coexistence;cognitions; conservation;methods; quantitativesurvey; toleranceIntroduction and Literature ReviewHuman dimensions of wildlife researchers have increasingly sought to define and oper-ationalize concepts relating to human–wildlife interactions, including people’s cognitionstoward species and their evaluations of wildlife-related costs and benefits (Carlson et al.,2023; König et al., 2020). Despite this literature, or perhaps because of it (Bruskotter et al.,2015), wildlife scientists and practitioners continue to hold shared, contested, and some-times confused perspectives toward concepts such as tolerance, acceptability, coexistence,and other cognitions such as beliefs, attitudes, and behavioral intentions when used withregard to wildlife (Glikman et al., 2021; Hill, 2021). Universally shared definitions of theseCONTACT Alexander L. Metcalf alex.metcalf@umontana.edu Wildlife Biology Degree Program, Department ofSociety & Conservation, W.A. Franke College of Forestry & Conservation, University of Montana, 440 CHCB, 32 Campus Drive,Missoula, MT 59812, USAThis article has been republished with a minor change. This change does not impact on the academic content of the article.HUMAN DIMENSIONS OF WILDLIFE2025, VOL. 30, NO. 4, 415–429https://doi.org/10.1080/10871209.2024.2318330© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided theoriginal work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. Major findings: Researchers developed a new tool called the "wildlife attitude-acceptability framework" to better understand how people think about large predators like wolves, cougars, and grizzly bears. By looking at whether people like the animal (their attitude) and whether they are okay with the impacts the animal has on their lives (their acceptability), the researchers identified four main groups: advocates, conditional supporters, opponents, and those who tolerate the species. The study found that while many people like these animals, they are often "conditional supporters" who only want them around if they don't cause too much trouble. This framework helps wildlife managers set clearer goals and create better plans for helping humans and wildlife coexist." assertion.
- s43621-024-00253-y comment "Beef production systems are at the center of ongoing discussion and debate on food systems sustainability. There is a growing interest among beef producers, consumers, and other beef supply chain stakeholders in achieving greater sustainability within the industry, but the relationship of this interest to general sustainability issues such as climate change, biodiversity loss, food security, livelihood risks, and animal welfare concerns is unclear. Specifically, there is very little research documenting how beef producers define and view the concept of sustainability and how to achieve it. Producer perspectives are critical to identifying constraints to sustainability transitions or to help build agreement with other producers about the shared values such transitions may support. Through a secondary analysis of survey data of U.S. beef producers (n = 911) conducted in 2021 by the Trust in Food division of Farm Journal, a corporation that provides content, data, and business insights to the agricultural community (e.g., producers, processors/distributors, and retailers), we investigated what “sustainable beef” means to U.S. beef producers, highlighting the key components and constraints they perceive to achieving desirable sustainability outcomes. Leveraging the three-pillar model of sustainability as a framework for analysis, we identified key themes producers use to define “sustainable beef.” We found that producers collectively viewed sustainability as: (1) multidimensional and interconnected; (2) semi-closed and regenerative; (3) long-lasting; and (4) producer-centered, although an integrated perspective uniting these aspects was rare. We discuss how these perspectives may be the basis for sustainability efforts supported by producers and raise future research considerations toward a shared understanding of what sustainability is and what is needed for enduring sustainability solutions in the U.S. beef industry. Major findings: A 2021 survey of 911 U.S. beef producers investigated how those in the industry define "sustainable beef." The study found that 71% (n = 649) of respondents came from families with three or more generations in beef production. Producers collectively identified sustainability through four themes: it is multidimensional (covering environmental, social, and economic needs), semi-closed (minimizing outside inputs), long-lasting (focused on the future), and producer-centered (focused on fair markets). While 49% of responses mentioned environmental topics and 43% discussed economic viability, only 5% of producers expressed a fully integrated view including all three "pillars" of sustainability. The research highlights that while the industry accounts for 17% of total U.S. agricultural cash receipts ($72.9 billion in 2021), producers face significant external constraints, such as market consolidation where the four largest packing firms control roughly 75% of the market." assertion.
- 2023GL105811 comment "Climate change has led to an increase in the frequency and size of wildfires in the Western United States. The gases and particles released from wildfires impact air quality and climate, so it is important to understand the chemical composition of these emissions. In current air quality forecasts and climate models, the composition of wildfire emissions is based on the dominant vegetation burned and is assumed to be constant over time. In contrast, measurements from laboratory burns indicate that the composition of emissions from fires changes over time, as fires progress from more flaming combustion to flameless burning dominated by smoke (smoldering). It is challenging to have daily field measurements of the emissions from long-lived wildfires, but there are instruments in space that can make daily observations of wildfires globally. In this study, we show how the composition of emissions from wildfires in California, Oregon, and Washington changed over time, as they progressed from more flaming to more smoldering combustion, using observations from a satellite instrument called TROPOMI. The analysis of the composition of wildfire emissions and their evolution over time using TROPOMI could improve air quality forecasting and climate modeling globally. Major findings: A 2023 study used the TROPOMI satellite instrument to track how the chemical composition of wildfire smoke changes as fires evolve. By analyzing 15 large wildfires in the Western U.S., researchers discovered that the ratio of nitrogen dioxide (NO2) to carbon monoxide (CO) drops significantly as a fire moves from its "flaming" stage to its "smoldering" stage. This is a vital finding because current air quality forecasts often assume smoke composition stays the same throughout a fire's duration. Using daily satellite observations allows scientists to see these shifts in real-time, even when ground-based measurements are unavailable. This information helps improve models that predict how wildfire smoke will impact public health and the global climate over several weeks." assertion.
- S1550742423000830 comment "Rangelands across the world are facing rapid and unprecedented social and ecological change. In the US West, sustaining the ecological and economic integrity of rangelands across both public and private lands depends largely on ranchers who make adaptive decisions in the face of variability and uncertainty. In this study, we build on previous conceptualizations of adaptive decision making that situate individual-level decisions within complex rangeland social-ecological systems. We surveyed 450 (36% response rate) Montana ranchers to gain insight into how key factors influenced adaptive decision making, specifically in the context of ongoing drought and climate-related change affecting rangeland ecology and productivity. We predicted that ranchers’ management goals, their use of information sources, and their use of monitoring would significantly influence the use of adaptive practices, with monitoring mediating the relationship between the explanatory and response variables. We tested these predictions using a path model analysis and found that management goals related to both stewardship and profit/production, the number of information sources used, and monitoring were all significantly and positively related to ranchers’ use of adaptive management practices. Interestingly, we found that these factors were hierarchical with monitoring and the use of information was the strongest predictor while management goals were secondary. The significant, mediating effect of monitoring on the use of adaptive practices suggests that monitoring may be an important means for providing ranchers with useful and timely information about rangeland condition that is needed to adjust their actions, meet their management goals, and adapt to drought and climate-related change. We argue there is a need to better understand the efficacy of monitoring designs—of what, by whom, and how—for adaptive decision making, and we discuss other considerations related to the provision of useful drought and climate information for adaptive decision making based on our findings. Major findings: A 2023 study of 450 Montana ranchers explored how they make management decisions during periods of drought and climate change. The researchers found that while a rancher's goals (like taking care of the land or making a profit) and their sources of information are important, the single strongest predictor of whether they use "adaptive" management practices is whether they have a formal monitoring program. Monitoring acts as a "feedback loop," giving ranchers the data they need to adjust their grazing plans or water usage in real-time. Even though monitoring is incredibly helpful for keeping land healthy, fewer than half of the ranchers surveyed (42.9%) currently use a formal system, suggesting that helping ranchers with the cost and time of monitoring could greatly improve how rangelands are managed in the future." assertion.
- d3ea00098b comment "Formic acid (FA) and acetic acid (AA), two of the most abundant organic acids in the atmosphere, are typically underestimated by atmospheric models. Here we investigate their emissions, chemistry, and measurement uncertainties in biomass burning smoke sampled during the WE-CAN and FIREX-AQ aircraft campaigns. Our observed FA emission ratios (ERs) and emission factors (EFs) were generally higher than the 75th percentile of literature values, with little dependence on fuel type or combustion efficiency. Rapid in-plume FA production was observed (2.7 ppb ppmCO−1 h−1), representing up to ∼20% of the total emitted reactive organic carbon being converted to FA within half a day. AA ERs and EFs showed good agreement with the literature, with little or no secondary production observed within <8 hours of plume aging. Observed FA and AA trends in the near-field were not captured by a box model using the explicit Master Chemical Mechanism nor simplified GEOS-Chem chemistry, even after tripling the model's initial VOC concentrations. Consequently, the GEOS-Chem chemical transport model underestimates both acids in the western U.S. by a factor of >4. This is likely due to missing secondary chemistry in biomass burning smoke and/or coniferous forest biogenic emissions. This work highlights uncertainties in measurements (up to 100%) and even large unknowns in the chemical formation of organic acids in polluted environments, both of which need to be addressed to better understand their global budget. Major findings:Using data from the WE-CAN and FIREX-AQ aircraft campaigns, researchers discovered that formic acid emissions and secondary production in wildfire smoke are 3.5 times higher than previously reported in scientific literature. Despite these significantly higher observed levels, current global atmospheric models (such as GEOS-Chem) still underestimate formic and acetic acids by more than a factor of four. This study suggests that models are missing key chemical pathways—including secondary production from unknown precursors in biomass burning and biogenic emissions from coniferous forests—which are critical for accurately predicting air quality and cloud chemistry during wildfire seasons." assertion.
- 620 comment "Post-industrial communities across the world are transitioning from industrial economies and identities to an uncertain future. Their successful transitions depend on communities’ abilities to navigate change and maintain a quality of life, or their community’s resilience. Previous scholarship offers resources and capabilities that facilitate or inhibit community resilience such as leadership, social capital, and information. However, collective memory is not well integrated within the community resilience literature. Drawing on data from interviews with 33 community leaders in the town of Anaconda, Montana, we illuminate the impact of collective memory on community resilience. The AnacondaSmelter Stack stands out as a specific landmark and prominent feature of the built environment that perpetuates particular collective memories in Anaconda. We find that collective memory is an integral part of community resilience, where memories can aid in a community’s recovery and rebuilding or constrain thinking and divide viewpoints. We argue that ignoring collective memory’s connections to resilience can undermine efforts to face changes in these communities. Keywords: Community resilience, collective memory, post-industrial towns, mining A 2023 study of Anaconda, Montana, explores how a community's "collective memory" of its industrial past—symbolized by the iconic 585-foot Smelter Stack—impacts its ability to transition to a new future. Researchers found that while these shared memories can be a source of pride and a "galvanizing force" for recovery, they can also act as a constraint that divides viewpoints and makes it harder for the town to embrace new economic paths like tourism. The study concludes that ignoring these deep emotional ties to history can undermine efforts to build community resilience, suggesting that successful transitions in post-industrial towns require cleanups and development plans that are "historically informed" and respect the community's lived experiences. Major Findings: A 2023 study of Anaconda, Montana, explores how a community's "collective memory" of its industrial past—symbolized by the iconic 585-foot Smelter Stack—impacts its ability to transition to a new future. Researchers found that while these shared memories can be a source of pride and a "galvanizing force" for recovery, they can also act as a constraint that divides viewpoints and makes it harder for the town to embrace new economic paths like tourism. The study concludes that ignoring these deep emotional ties to history can undermine efforts to build community resilience, suggesting that successful transitions in post-industrial towns require cleanups and development plans that are "historically informed" and respect the community's lived experiences." assertion.
- 2403.10730 comment "In Precision Agriculture, the utilization of management zones (MZs) that take into account within-field variability facilitates effective fertilizer management. This approach enables the optimization of nitrogen (N) rates to maximize crop yield production and enhance agronomic use efficiency. However, existing works often neglect the consideration of responsivity to fertilizer as a factor influencing MZ determination. In response to this gap, we present a MZ clustering method based on fertilizer responsivity. We build upon the statement that the responsivity of a given site to the fertilizer rate is described by the shape of its corresponding N fertilizer-yield response (N-response) curve. Thus, we generate N-response curves for all sites within the field using a convolutional neural network (CNN). The shape of the approximated N-response curves is then characterized using functional principal component analysis. Subsequently, a counterfactual explanation (CFE) method is applied to discern the impact of various variables on MZ membership. The genetic algorithm-based CFE solves a multi-objective optimization problem and aims to identify the minimum combination of features needed to alter a site's cluster assignment. Results from two yield prediction datasets indicate that the features with the greatest influence on MZ membership are associated with terrain characteristics that either facilitate or impede fertilizer runoff, such as terrain slope or topographic aspect. Major findings:Researchers at Montana State University developed a new method for creating "management zones" in farm fields by using artificial intelligence to predict how crops will respond to nitrogen fertilizer. Unlike older methods that only look at historical yields, this approach uses a neural network to generate "N-response curves"—graphs showing how yield changes as fertilizer increases—for every spot in a field. To make the AI's decisions easier to understand, the researchers used "counterfactual explanations," which essentially ask: "What would have to change for this spot to behave differently?" The study found that terrain features like slope and soil moisture are the most important factors; for example, steep slopes often lead to fertilizer runoff, which makes those areas less responsive to treatment. This helps farmers apply fertilizer more accurately, saving money and reducing environmental impact." assertion.
- 10365540 comment "Accurate uncertainty quantification is necessary to enhance the reliability of deep learning (DL) models in real-world applications. In the case of regression tasks, prediction intervals (PIs) should be provided along with the deterministic predictions of DL models. Such PIs are useful or “high-quality (HQ)” as long as they are sufficiently narrow and capture most of the probability density. In this article, we present a method to learn PIs for regression-based neural networks (NNs) automatically in addition to the conventional target predictions. In particular, we train two companion NNs: one that uses one output, the target estimate, and another that uses two outputs, the upper and lower bounds of the corresponding PI. Our main contribution is the design of a novel loss function for the PI-generation network that takes into account the output of the target-estimation network and has two optimization objectives: minimizing the mean PI width and ensuring the PI integrity using constraints that maximize the PI probability coverage implicitly. Furthermore, we introduce a self-adaptive coefficient that balances both objectives within the loss function, which alleviates the task of fine-tuning. Experiments using a synthetic dataset, eight benchmark datasets, and a real-world crop yield prediction dataset showed that our method was able to maintain a nominal probability coverage and produce significantly narrower PIs without detriment to its target estimation accuracy when compared to those PIs generated by three state-of-the-art neural-network-based methods. In other words, our method was shown to produce higher quality PIs. Major findings:The DualAQD framework produces "prediction intervals" that inform users of the confidence level associated with an AI model's specific estimate. This method generates narrower and more accurate confidence ranges than existing methods while maintaining high overall target accuracy. The system successfully identifies regions of high uncertainty in crop yield predictions, increasing the reliability of deep learning models for high-stakes decision-making." assertion.
- 2023JD039309 comment "Crop residue and prescribed fires emit pollution that impacts air quality. FIREX-AQ provided observations of these emissions to better characterize their variability with a detailed set of chemical observations. These observations showed significant differences in the emissions from burning different crops (corn, rice, soybean, wheat) compared to other prescribed fires or grasslands that may be due to differences in the fuel composition, the use of agricultural chemicals, and moisture levels. Overall, FIREX-AQ observations for crop residue fires compared better with previous results in the region than with globally averaged information. The campaign observed even greater variability across EFs than previous studies, suggesting that new methods must be developed to take this into account to improve predictions of the air quality impacts of burning these fuels. Major findings:The FIREX-AQ campaign provided a comprehensive chemical characterization of 53 crop residue and 22 prescribed fires in the Eastern United States to establish regionally specific emission factors. Research revealed that corn residue burns at a significantly higher modified combustion efficiency than rice or soybean residues, leading to distinct emission profiles. The study identified twenty-three chemical species where crop residue emissions differed by over fifty percent from prescribed fires at similar combustion efficiencies, noting higher levels of nitrogen, halogens, and markers related to agricultural chemical use. Conversely, prescribed fires released ten times more monoterpenes than agricultural residues due to the presence of stored plant resins in woody biomass. These findings indicate that fuel-specific and regionally specific data are essential for reducing uncertainty in air quality models that previously relied on global averages." assertion.
- acs.est.3c05017?ref=article_openPDF comment "Biomass burning particulate matter (BBPM) affects regional air quality and global climate, with impacts expected to continue to grow over the coming years. We show that studies of North American fires have a systematic altitude dependence in measured BBPM normalized excess mixing ratio (NEMR; ΔPM/ΔCO), with airborne and high-altitude studies showing a factor of 2 higher NEMR than ground-based measurements. We report direct airborne measurements of BBPM volatility that partially explain the difference in the BBPM NEMR observed across platforms. We find that when heated to 40− 45 °C in an airborne thermal denuder, 19% of lofted smoke PM1 evaporates. Thermal denuder measurements are consistent with evaporation observed when a single smoke plume was sampled across a range of temperatures as the plume descended from 4 to 2 km altitude. We also demonstrate that chemical aging of smoke and differences in PM emission factors can not fully explain the platformdependent differences. When the measured PM volatility is applied to output from the High Resolution Rapid Refresh Smoke regional model, we predict a lower PM NEMR at the surface compared to the lofted smoke measured by aircraft. These results emphasize the significant role that gas-particle partitioning plays in determining the air quality impacts of wildfire smoke. KEYWORDS: Biomass burning organic aerosol volatility, volatility basis set. Major findings: Research regarding biomass burning organic aerosol volatility reveals a systematic altitude dependence in particulate matter concentrations, with airborne studies recording values twice as high as ground-based measurements. Direct airborne quantification demonstrates that approximately 19% of lofted smoke particulate matter evaporates when subjected to surface-level temperatures. These findings indicate that gas-particle partitioning, rather than chemical aging or initial emission factors, is the primary driver of platform-dependent differences in smoke density. Applying these volatility constraints to regional models reduces predicted surface smoke concentrations by 31%, aligning model outputs more closely with observed ground-level impacts." assertion.
- assertion comment "I retract this role, bacause it is not relevant for this workshop." assertion.
- trrust comment "A manually curated database of human and mouse transcriptional regulatory networks. Contains 8427 human transcriptional regulatory interactions including 2428 activation, 1581 repression, and 4418 unknown interactions." assertion.
- dataset comment "1500 synthetic networks generated for benchmarking QOMIC algorithm. Networks vary in size (200-1000 nodes), density (average degree 2-10), and activation ratio (0.2, 0.5, 0.8) across four motif topologies: cascade, FFL, bifan, and biparallel." assertion.
- article12-8 comment "mentions icons" assertion.