Nanopublications LDF server

Matches in Nanopublications for { ?s <http://purl.org/dc/terms/hasQualityAssessment> ?o ?g. }

• studyAssessmentDataset hasQualityAssessment "AI-assisted prioritization with subjective human decision-making. ASReview LAB uses active learning to prioritize papers most likely to be relevant, while human reviewer makes all inclusion/exclusion decisions based on subjective assessment of potential relevance. Bias considerations: Single screener's subjective interpretation of 'potential relevance'; Active learning may miss relevant papers in unexplored regions of feature space; No inter-rater reliability assessment; English language bias; Inclusive approach may result in heterogeneous study population." assertion.
• studyAssessmentDataset hasQualityAssessment "test..." assertion.
• studyAssessmentDataset hasQualityAssessment "Papers assessed for relevance to PICO criteria and quality of evidence for quantum computing applications in biodiversity research. Assessment criteria included: (1) Clarity of methods - well-documented quantum algorithms and experimental setup, (2) Strength of evidence for computational advantages - quantitative comparisons between quantum and classical approaches (e.g., 19/20 performance comparisons), (3) Transferability to biodiversity contexts - explicit discussion of ecological/biodiversity applications or clear methodological parallels between demonstrated applications (disease networks) and target applications (ecological networks), (4) Hardware requirements documentation - discussion of current limitations (NISQ devices, qubit counts, partitioning strategies) and practical workarounds, (5) Transparency about limitations - acknowledgment of challenges such as state preparation bottlenecks, performance variability, and dependency on future hardware improvements. Both selected papers provided high-quality evidence across multiple evaluation dimensions with clear documentation of both advantages and limitations." assertion.
• studyAssessmentDataset hasQualityAssessment "AI-assisted prioritization with subjective human decision-making. ASReview LAB uses active learning to prioritize papers most likely to be relevant, while human reviewer makes all inclusion/exclusion decisions based on subjective assessment of potential relevance. Bias considerations: Single screener's subjective interpretation of 'potential relevance'; Active learning may miss relevant papers in unexplored regions of feature space; No inter-rater reliability assessment; English language bias; Inclusive approach may result in heterogeneous study population." assertion.