{"id":"W6945407002","doi":"10.25318/9810056101-eng","title":"Long-form data quality indicators for immigration, place of birth, and citizenship: Canada, provinces and territories, census metropolitan areas, census agglomerations and census subdivisions","year":2023,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Urban agglomeration; Metropolitan area; American Community Survey; Data quality; 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":[0.001843587,0.001629393,0.00193362,0.005381497,0.001951757,0.002628126,0.003929981,0.00130215,0.03631707],"category_scores_gemma":[0.01455635,0.001107374,0.001394696,0.0246526,0.0005619459,0.001093615,0.001581935,0.002833765,0.01783039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02450718,"about_ca_system_score_gemma":0.05757858,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9738583,"about_ca_topic_score_gemma":0.9793382,"domain_scores_codex":[0.997411,0.0001871951,0.0003479354,0.0003890884,0.001036483,0.0006284004],"domain_scores_gemma":[0.986632,0.0008958212,0.0008970762,0.0006752946,0.009910935,0.0009889648],"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":[0.00003234136,0.00001358358,0.003050244,0.0002214781,0.00003289854,0.000009898123,0.00002534598,0.0001715117,0.00001305866,0.0004681105,0.9944852,0.001476411],"study_design_scores_gemma":[0.0004510567,0.00001696674,0.1197584,0.001141841,0.0001192616,0.00006528636,0.0004063011,0.001035814,0.000318703,0.001367441,0.875226,0.00009306172],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001160644,0.00003190008,0.00003096875,0.00004899798,0.00001125734,0.00001497198,0.9993254,0.00002977446,0.0003906707],"genre_scores_gemma":[0.001060344,0.00008063858,0.0002661562,0.00005793851,0.000005770466,0.0001222357,0.9971868,0.00002813923,0.001191843],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03631707,"threshold_uncertainty_score":0.1778129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02267976853724998,"score_gpt":0.2981041293092778,"score_spread":0.2754243607720278,"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."}}