{"id":"W4383264353","doi":"10.1158/1055-9965.23627254.v1","title":"Supplementary Table 4 from Site-Specific Cancer Incidence by Race and Immigration Status in Canada 2006–2015: A Population-Based Data Linkage Study","year":2023,"lang":"en","type":"supplementary-materials","venue":"","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Race (biology); Immigration; Table (database); Linkage (software); Demography; Cancer incidence; Record linkage; Incidence (geometry); Geography; Population; Gerontology; Genealogy; Medicine; Sociology; Gender studies; History; Genetics; Database; Biology; Computer science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001476766,0.0009043593,0.001137626,0.004428767,0.002224338,0.002161054,0.002270876,0.0007283874,0.3553875],"category_scores_gemma":[0.02720153,0.0008107291,0.001314424,0.01375347,0.0004217982,0.001111019,0.001558028,0.001228237,0.03459283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007537319,"about_ca_system_score_gemma":0.02303811,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8510399,"about_ca_topic_score_gemma":0.9053783,"domain_scores_codex":[0.9977297,0.0001990359,0.0003937203,0.0004559362,0.0008129725,0.0004086647],"domain_scores_gemma":[0.9792388,0.004798483,0.001417932,0.001322852,0.0119708,0.001251185],"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":[0.00007209704,0.00002440572,0.009824246,0.0007268611,0.0001031608,0.00003737019,0.00005684969,0.0001614079,0.00003211965,0.0003276163,0.9839643,0.004669392],"study_design_scores_gemma":[0.001040046,0.00006197408,0.257568,0.003210743,0.0005173906,0.0004139803,0.0007299655,0.0009055219,0.0004001682,0.001877366,0.7331368,0.0001380306],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003845385,0.00006531521,0.0000995693,0.00008284127,0.00004929362,0.00005495001,0.9978282,0.00005458591,0.001380787],"genre_scores_gemma":[0.007277078,0.0003128683,0.001126178,0.0004080432,0.00006379576,0.0005029574,0.9828029,0.0001802425,0.007325991],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3553875,"threshold_uncertainty_score":0.9194615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05551991150630087,"score_gpt":0.3427086021283916,"score_spread":0.2871886906220907,"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."}}