{"id":"W7100003138","doi":"","title":"in the United States &amp;amp; Canada Multi-Registry Cancer Incidence and Mortality Data in","year":2005,"lang":"en","type":"article","venue":"","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Comparability; Cancer incidence; Incidence (geometry); Cancer; Data quality; Cancer registry; Quality (philosophy)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001566837,0.0003908702,0.0005081335,0.004322549,0.002266672,0.002554908,0.001167824,0.000541946,0.03441082],"category_scores_gemma":[0.006328349,0.0003774243,0.0004766058,0.01010105,0.0003403995,0.0008295718,0.001199859,0.0009498024,0.008965702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02856989,"about_ca_system_score_gemma":0.09447958,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9855318,"about_ca_topic_score_gemma":0.9912141,"domain_scores_codex":[0.9977654,0.0001801242,0.0001662553,0.0002830293,0.001211623,0.0003935286],"domain_scores_gemma":[0.99157,0.0003640277,0.0004054815,0.0003992743,0.006307008,0.0009541769],"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":[0.00004745764,0.00001568214,0.02134584,0.0001203327,0.00003158193,0.00004193385,0.0001205639,0.0002913222,0.00004867191,0.005340943,0.9207374,0.05185819],"study_design_scores_gemma":[0.00003107584,0.0000109389,0.1075978,0.0001984689,0.00003961673,0.00006727205,0.0003257946,0.000916293,0.0002151066,0.0008866718,0.8896798,0.00003115442],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01207822,0.004480582,0.002864909,0.01885441,0.001270489,0.000355427,0.7906168,0.001143233,0.168336],"genre_scores_gemma":[0.1308999,0.007316531,0.01784937,0.007074207,0.0005387778,0.0008585633,0.5247433,0.0006628095,0.3100565],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03441082,"threshold_uncertainty_score":0.20729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08431882123114645,"score_gpt":0.3453859935001825,"score_spread":0.2610671722690361,"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."}}