{"id":"W3144783246","doi":"","title":"Bayesian hierarchical models for mapping lung cancer mortality in Ontario","year":2000,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lung cancer; Bayesian probability; Statistics; Medicine; Computer science; Oncology; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":{"n_in":0,"stratum":"about_only","weight":3321.24,"opus":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"low","reason":"Dissertation on Bayesian disease mapping of Ontario lung cancer mortality; abstract is missing, and the title reads as applied epidemiology rather than research-on-research."},"gpt":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"low","reason":"The title concerns mapping lung cancer mortality with Bayesian models, while the abstract is insufficient to add detail."},"grok":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"medium","reason":"Uses Bayesian hierarchical models to map lung-cancer mortality; method-as-tool for epidemiology, not study of the method."}},"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003494295,0.0007344339,0.001062511,0.001823157,0.002466354,0.001916208,0.002392093,0.001285858,0.005781192],"category_scores_gemma":[0.01171212,0.00129009,0.001493074,0.002836175,0.001401298,0.0009729652,0.00129507,0.001249336,0.0006849333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01630559,"about_ca_system_score_gemma":0.01185342,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9446106,"about_ca_topic_score_gemma":0.9549127,"domain_scores_codex":[0.9989728,0.000481677,0.00004647364,0.0002038123,0.0001305879,0.000164722],"domain_scores_gemma":[0.9968046,0.00209116,0.0002323402,0.000198354,0.0005185517,0.000155068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000453655,0.00009828425,0.0936271,0.000155175,0.0005042421,0.0002783215,0.001651314,0.7844159,0.0006854008,0.04740134,0.01850483,0.05222442],"study_design_scores_gemma":[0.0001015537,0.00002907608,0.04862867,0.00007174737,0.0001336899,0.00003988075,0.0006199948,0.9193203,0.0001940431,0.02373618,0.007055239,0.00006960004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6701183,0.004509302,0.2690509,0.01045537,0.000251598,0.0004776145,0.02259113,0.001008003,0.02153773],"genre_scores_gemma":[0.9452556,0.001524009,0.02400388,0.0001687701,0.00007901159,0.0002248938,0.006185132,0.0001692639,0.02238952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0553894,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06158385267141387,"score_gpt":0.3398724076585228,"score_spread":0.2782885549871089,"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."}}