{"id":"W2891465197","doi":"10.23889/ijpds.v3i4.615","title":"Statistical Population Register: using administrative in the Canadian Census","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Census and Population Estimation","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Census; Population; Scope (computer science); Register (sociolinguistics); Geography; Data quality; Computer science; Statistics; Operations management; Engineering; Medicine; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0115062,0.001115209,0.0008441492,0.01636288,0.004783369,0.007877761,0.003266533,0.0007438982,0.03730474],"category_scores_gemma":[0.05114525,0.0007617119,0.001051674,0.03699262,0.001043564,0.002481271,0.003491538,0.001622255,0.01499118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04271606,"about_ca_system_score_gemma":0.1442845,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9697715,"about_ca_topic_score_gemma":0.9699498,"domain_scores_codex":[0.9794133,0.003382144,0.00172741,0.001452656,0.01244369,0.001580865],"domain_scores_gemma":[0.9551103,0.002589097,0.001834459,0.002695897,0.03637085,0.001399397],"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":[0.00008585673,0.00004061565,0.01980663,0.001393466,0.00004836281,0.0001130472,0.001499971,0.001209793,0.0005486839,0.02412159,0.6968803,0.2542517],"study_design_scores_gemma":[0.00002358787,0.00002335815,0.04939972,0.0007267186,0.00003528795,0.00008161709,0.001488,0.001203571,0.0007219698,0.001851979,0.9443476,0.0000966282],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01441884,0.005314758,0.06774967,0.01343629,0.003422822,0.01102775,0.5930973,0.00727739,0.2842552],"genre_scores_gemma":[0.1303004,0.01802437,0.2663931,0.004987783,0.001294143,0.01210926,0.4467742,0.003591068,0.1165256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04271606,"threshold_uncertainty_score":0.3099282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4095750772850147,"score_gpt":0.5289055900918229,"score_spread":0.1193305128068082,"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."}}