{"id":"W4309264283","doi":"10.1371/journal.pcbi.1010649","title":"Molecular source attribution","year":2022,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Salmonella and Campylobacter epidemiology","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Western University","funders":"Canadian Institutes of Health Research","keywords":"Attribution; Outbreak; Infectious disease (medical specialty); Population; Biology; Inference; Disease; Transmission (telecommunications); Computer science; Computational biology; Evolutionary biology; Genetics; Medicine; Environmental health; Artificial intelligence; Virology; Psychology; Pathology; Telecommunications; Social psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.01369672,0.003177583,0.002682716,0.008757317,0.002688498,0.01108251,0.00578401,0.006081115,0.265283],"category_scores_gemma":[0.1037081,0.002035654,0.0031733,0.009499939,0.002430484,0.008030574,0.009227906,0.005410461,0.1652871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001723547,"about_ca_system_score_gemma":0.006268732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001551597,"about_ca_topic_score_gemma":0.001018943,"domain_scores_codex":[0.989391,0.002362073,0.001507521,0.002657374,0.003068508,0.001013372],"domain_scores_gemma":[0.9448832,0.01799889,0.004446038,0.02174832,0.008657506,0.002265954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0009092689,0.0001187019,0.004067961,0.00359759,0.000327699,0.0009853097,0.0005658719,0.003341634,0.003709822,0.08076897,0.7497919,0.1518153],"study_design_scores_gemma":[0.0002692203,0.00007883098,0.001256096,0.001596035,0.0001544475,0.001113635,0.0002982618,0.01676146,0.007221069,0.1290726,0.8419524,0.000225778],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003847142,0.00364962,0.489949,0.009142526,0.02978458,0.0008737479,0.2025105,0.1972984,0.06294444],"genre_scores_gemma":[0.1077542,0.006810943,0.374749,0.00583446,0.007435956,0.003166145,0.3595453,0.0737145,0.06098951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.265283,"threshold_uncertainty_score":0.8874599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02844726720961721,"score_gpt":0.2338375923842796,"score_spread":0.2053903251746624,"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."}}