Matches in Nanopublications for { ?s <http://www.w3.org/2000/01/rdf-schema#comment> ?o ?g. }
- RA7XVA-qxN_CFEOrl5I_M3-1okgE8LhHCd5FlrCg36UY8 comment "Hub nanopub linking to granular assertions about CoLoC-seq methodology, kinetics model, and validation" pubinfo.
- interactoa.zbmed.de comment "Web application for exploring sRNA regulatory networks from open-access literature" assertion.
- assertion comment "InteractOA is defined as a web application in the source publication" provenance.
- RA81mzVzbfb3Zfjcd6RYyZdJQmQyCbeLGS1ozsVptl8cs comment "Name label for the InteractOA application" pubinfo.
- assertion comment "Label identified in the InteractOA publication" provenance.
- assertion comment "Description from the InteractOA publication abstract and introduction" provenance.
- assertion comment "Primary URL where InteractOA application is hosted" provenance.
- assertion comment "InteractOA is built on and leverages Wikidata as a knowledge base" provenance.
- assertion comment "InteractOA is licensed under Creative Commons Attribution 4.0 International" provenance.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.