{"id":"W2037712084","doi":"10.1503/cmaj.080760","title":"Cancer in Canada in 2008","year":2008,"lang":"en","type":"article","venue":"Canadian Medical Association Journal","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Cancer Society; Ontario Institute for Cancer Research; Cancer Care Ontario","funders":"","keywords":"Cancer; Context (archaeology); Data science; Computer science; Medicine; Geography; Archaeology; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.001156698,0.000714841,0.0009152218,0.007347725,0.003714297,0.002040498,0.001847555,0.0005751021,0.01075652],"category_scores_gemma":[0.003849332,0.0004436822,0.001016015,0.0144234,0.0002928896,0.000563421,0.001388999,0.001144679,0.002078357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05518341,"about_ca_system_score_gemma":0.07095716,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9905745,"about_ca_topic_score_gemma":0.9968895,"domain_scores_codex":[0.9980895,0.00010564,0.0001489387,0.0002441207,0.0009166335,0.0004950704],"domain_scores_gemma":[0.9965424,0.00006004725,0.0002039998,0.00007396666,0.00274352,0.0003761198],"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":[0.0003713953,0.0001348928,0.4007262,0.001557823,0.000276864,0.0005736555,0.00139965,0.0008233567,0.0002690433,0.003877548,0.4938545,0.09613517],"study_design_scores_gemma":[0.00009268858,0.00007291671,0.6210113,0.001002712,0.0002604287,0.0005401213,0.00201418,0.001084585,0.0003782814,0.0004873092,0.372997,0.00005833181],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1019069,0.01292548,0.002411695,0.005149367,0.0009578249,0.003534514,0.7963867,0.0005046868,0.07622281],"genre_scores_gemma":[0.3241062,0.01799328,0.01771653,0.00852921,0.0005203489,0.008021729,0.5129346,0.0002657321,0.1099123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05518341,"threshold_uncertainty_score":0.4003856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009461005071532786,"score_gpt":0.2334301250325111,"score_spread":0.2239691199609783,"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."}}