{"id":"W4389234955","doi":"10.1371/journal.pone.0273205","title":"Improving estimates of pertussis burden in Ontario, Canada 2010–2017 by combining validation and capture-recapture methodologies","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Vector Institute; Institute for Clinical Evaluative Sciences; Public Health Ontario; University of Toronto","funders":"Canadian Institutes of Health Research; World Health Organization","keywords":"Mark and recapture; False positive paradox; Statistics; Medicine; Demography; Environmental health; Mathematics; Population","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003544546,0.0001320054,0.000447028,0.00009552059,0.0000370114,0.00001457354,0.00009575987,0.00007231941,0.00005964536],"category_scores_gemma":[0.001529044,0.0001206261,0.00002195558,0.0001634343,0.00005443735,0.00009131527,0.00009273223,0.0002048184,0.000002998531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001624834,"about_ca_system_score_gemma":0.0003864308,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9442036,"about_ca_topic_score_gemma":0.8336551,"domain_scores_codex":[0.9988923,0.00006978879,0.0002448496,0.0002569161,0.000314607,0.0002215279],"domain_scores_gemma":[0.9989963,0.0004329242,0.0001242362,0.0002753659,0.00007965607,0.00009157689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002557869,0.0002451636,0.6748154,0.00112021,0.0004872819,0.0001148599,0.002230861,0.00006910461,0.2938702,0.00001314714,0.02190213,0.004875861],"study_design_scores_gemma":[0.002167743,0.0001525787,0.9220212,0.001049584,0.0004543241,0.000006372347,0.001595413,0.007134476,0.0645769,0.0001876315,0.0002488847,0.0004049693],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974931,0.0007003121,0.00003191645,0.0008915582,0.00006144715,0.0002781948,0.0001868767,0.00008430647,0.0002722763],"genre_scores_gemma":[0.9881581,0.00007566123,0.009893632,0.0000995421,0.0000218302,0.00003016389,0.0008467425,0.00002267696,0.0008516732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2472058,"threshold_uncertainty_score":0.4918991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07039522811402428,"score_gpt":0.2758204301386783,"score_spread":0.205425202024654,"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."}}