{"id":"W4241973015","doi":"10.4095/301555","title":"Visible Minority Population, 2006 - South Asian Population by Census Subdivision","year":2010,"lang":"en","type":"report","venue":"","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Subdivision; Geography; Population; Demography; Genealogy; History; Sociology; Archaeology","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":[0.0002927548,0.0007547181,0.000243065,0.001868606,0.0005558308,0.0007177783,0.0006958243,0.0002034524,0.01195255],"category_scores_gemma":[0.0008891501,0.0001918951,0.0004233592,0.003221993,0.0001137439,0.0008021165,0.0007868212,0.0005676221,0.009429143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001233078,"about_ca_system_score_gemma":0.00360449,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3159586,"about_ca_topic_score_gemma":0.3418759,"domain_scores_codex":[0.9997745,0.00001178761,0.00002228757,0.00002098296,0.0001148699,0.00005556718],"domain_scores_gemma":[0.9995193,0.0000120363,0.00006505738,0.00001364185,0.0003195662,0.00007034904],"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":[0.0001640375,0.0001140891,0.1459258,0.001126629,0.0000872033,0.0001973789,0.001903692,0.0005160721,0.0009580319,0.001831105,0.7882357,0.05894022],"study_design_scores_gemma":[0.00004296135,0.00005417824,0.7033803,0.0003363537,0.00003955144,0.0002553469,0.002807169,0.0005274416,0.0006162681,0.0002451129,0.2916702,0.00002504576],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.07764015,0.001505751,0.0004160762,0.0007546313,0.0003407182,0.0004335763,0.8427845,0.0003930314,0.07573166],"genre_scores_gemma":[0.1543278,0.007492682,0.002514593,0.0005898866,0.0001376325,0.001228357,0.7304883,0.0001698395,0.1030509],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6840414,"threshold_uncertainty_score":0.628239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02220442191041205,"score_gpt":0.3287756209341799,"score_spread":0.3065711990237679,"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."}}