{"id":"W2148952905","doi":"10.1016/j.jglr.2010.08.008","title":"A statistical approach for establishing tumor incidence delisting criteria in areas of concern: A case study","year":2010,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Pennsylvania Sea Grant, Pennsylvania State University","keywords":"Watershed; Incidence (geometry); Covariate; Bayesian probability; Statistics; Fish <Actinopterygii>; Bay; Sampling (signal processing); Chesapeake bay; Environmental science; Computer science; Fishery; Geography; Biology; Mathematics; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03180551,0.0006375014,0.000801044,0.007063879,0.001489879,0.00232603,0.001779802,0.001005229,0.002306371],"category_scores_gemma":[0.08772544,0.0003745063,0.001615509,0.004145879,0.002067872,0.001231883,0.001463667,0.000988821,0.0002633819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001776109,"about_ca_system_score_gemma":0.003720803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005379789,"about_ca_topic_score_gemma":0.00785774,"domain_scores_codex":[0.9795026,0.01403918,0.002107005,0.0008639686,0.003093219,0.0003939861],"domain_scores_gemma":[0.8924826,0.09129215,0.004693777,0.002860706,0.008028042,0.0006426596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001097137,0.0007537273,0.5094422,0.0005594446,0.0006563208,0.003148491,0.003069067,0.04981843,0.01086705,0.03812044,0.002672468,0.3797952],"study_design_scores_gemma":[0.0002176035,0.004213461,0.1939853,0.0002293622,0.001102386,0.01053793,0.01200469,0.6813977,0.01348598,0.0688878,0.0135366,0.0004011677],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4006426,0.0004319743,0.5889155,0.001011052,0.0000659398,0.001144902,0.0006240647,0.0003103899,0.006853536],"genre_scores_gemma":[0.7902699,0.0001093752,0.2082605,0.00006904078,0.00002648887,0.0004355008,0.0002426877,0.0000497655,0.0005368266],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03180551,"threshold_uncertainty_score":0.1682057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09884329615574926,"score_gpt":0.3992922933491169,"score_spread":0.3004489971933677,"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."}}