{"id":"W4398221244","doi":"10.1111/risa.14318","title":"Research gaps and priorities for quantitative microbial risk assessment (QMRA)","year":2024,"lang":"en","type":"article","venue":"Risk Analysis","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; RPM International (Canada); University of Waterloo","funders":"National Institute of General Medical Sciences; Centers for Disease Control and Prevention; U.S. Department of Agriculture; U.S. Environmental Protection Agency; U.S. Department of Defense; National Institutes of Health; National Science Foundation","keywords":"Context (archaeology); Risk analysis (engineering); Computer science; Management science; Engineering; Biology; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1262211,0.00198501,0.003487928,0.003823521,0.003443827,0.01569175,0.005670796,0.0123472,0.01278844],"category_scores_gemma":[0.1020614,0.001075733,0.003097164,0.00365819,0.00913657,0.02562635,0.01138131,0.01655833,0.003249105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01019034,"about_ca_system_score_gemma":0.05641991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009546447,"about_ca_topic_score_gemma":0.008885995,"domain_scores_codex":[0.9636109,0.02165022,0.003700586,0.003598234,0.005215121,0.002224901],"domain_scores_gemma":[0.7503565,0.2000468,0.007121624,0.006172423,0.02923787,0.007064815],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003386814,0.0002906617,0.002978716,0.01598448,0.0002160148,0.0003203583,0.003173642,0.00781808,0.002183326,0.2482762,0.07164694,0.6467728],"study_design_scores_gemma":[0.00004222243,0.0003454378,0.001948936,0.02793905,0.0002213671,0.0002916439,0.01344447,0.006483016,0.001619937,0.5318068,0.4155923,0.0002647967],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.004483274,0.3008879,0.0450949,0.6321463,0.006013911,0.0001803887,0.0007786094,0.00040224,0.01001245],"genre_scores_gemma":[0.1306616,0.5367525,0.2159197,0.1012034,0.009206396,0.0009326184,0.001593493,0.0003106107,0.003419775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8737789,"threshold_uncertainty_score":0.6675287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0813285220433047,"score_gpt":0.4561837612674202,"score_spread":0.3748552392241155,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}