{"id":"W6921142951","doi":"10.6084/m9.figshare.c.3605423_d1","title":"Additional file 1: Table S1. of Describing the linkages of the immigration, refugees and citizenship Canada permanent resident data and vital statistics death registry to Ontarioâ s administrative health database","year":2016,"lang":"en","type":"article","venue":"Figshare","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Table (database); Refugee; Citizenship; Matching (statistics); Health statistics; Process (computing); Cover (algebra); Confidentiality","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":[0.002072022,0.0009968124,0.001043478,0.00474884,0.001910091,0.001811062,0.00207902,0.0007065146,0.8697315],"category_scores_gemma":[0.0332602,0.0007815924,0.0006608788,0.009647434,0.0004520736,0.001561878,0.001206017,0.0008653566,0.2061446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004885002,"about_ca_system_score_gemma":0.01057539,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3298131,"about_ca_topic_score_gemma":0.4659494,"domain_scores_codex":[0.9989555,0.000155267,0.0001562822,0.0002125679,0.0003201032,0.0002002959],"domain_scores_gemma":[0.977396,0.01270449,0.0011786,0.001298298,0.006690365,0.0007322336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004042271,0.00001997795,0.00111754,0.0006124268,0.000008957687,0.00001803842,0.0000598827,0.0001219354,0.00002350241,0.0002460239,0.9939926,0.003738736],"study_design_scores_gemma":[0.0009454202,0.00004957473,0.03208773,0.002278721,0.00007934155,0.0001708707,0.0009158237,0.0008906774,0.0004214089,0.002693881,0.9593593,0.0001072968],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007247959,0.000006292078,0.0001088168,0.00004293683,0.00001222996,0.00007923515,0.9984382,0.00009971153,0.001140151],"genre_scores_gemma":[0.003946404,0.0001221171,0.002818344,0.0002648992,0.00004305899,0.00168423,0.977411,0.0006171779,0.01309289],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8697315,"threshold_uncertainty_score":0.6557868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1827933237632978,"score_gpt":0.3370242833233091,"score_spread":0.1542309595600113,"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."}}