{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002745418,0.0001172269,0.0001188815,0.0004343765,0.0009123398,0.0005708404,0.001270143,0.00005535132,0.0000555806],"category_scores_gemma":[0.002879018,0.00009164895,0.00002911884,0.0003949967,0.0001948441,0.001522215,0.00007660405,0.0001697302,0.000004476496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005931677,"about_ca_system_score_gemma":0.0003443955,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03437714,"about_ca_topic_score_gemma":0.2120317,"domain_scores_codex":[0.9977105,0.00009207023,0.0005931206,0.0003086637,0.001003109,0.0002925067],"domain_scores_gemma":[0.9981035,0.0002827985,0.0003714089,0.0004013069,0.000706982,0.0001339844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001212885,0.0001038296,0.1857135,0.00001198959,0.0000234647,0.00002496056,0.001009692,0.0003552705,0.00007293417,0.793845,0.004555651,0.01416239],"study_design_scores_gemma":[0.0005131349,0.00007011872,0.6356979,0.00006949413,0.00002461423,0.0003433937,0.0001621748,0.2129983,0.000009987149,0.1423888,0.007517381,0.0002046913],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9053433,0.00001359978,0.07889252,0.005287906,0.005578633,0.001004522,0.002164498,0.00003898004,0.001676017],"genre_scores_gemma":[0.9499713,0.000001424613,0.04816255,0.0002308713,0.0006862868,0.000003967009,0.0009075617,0.000009936422,0.00002608341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6514562,"threshold_uncertainty_score":0.9720531,"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."}}