{"id":"W7098992407","doi":"","title":"THE USE OF INDIVIDUAL ADMINISTRATIVE RECORDS FOR SOCIAL STATISTICAL PURPOSES 1N CANADA","year":2015,"lang":"en","type":"article","venue":"","topic":"Diatoms and Algae Research","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistical analysis; Official statistics; Census; Data collection; Government (linguistics)","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":[0.01105183,0.0007530075,0.0008851456,0.01188153,0.004116107,0.003102184,0.00354378,0.0005366092,0.02215265],"category_scores_gemma":[0.04631567,0.0007393038,0.0009090737,0.03048979,0.00105396,0.00104146,0.002842876,0.001842341,0.004286553],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02051727,"about_ca_system_score_gemma":0.08551037,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9660738,"about_ca_topic_score_gemma":0.9736463,"domain_scores_codex":[0.973041,0.006075874,0.002197794,0.001894509,0.01462655,0.002164304],"domain_scores_gemma":[0.935919,0.007628016,0.008692862,0.008252379,0.0372542,0.00225361],"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":[0.0001838576,0.0001594026,0.1124273,0.001914986,0.0004927,0.000163777,0.00191456,0.00150314,0.0004320265,0.04091791,0.6077663,0.232124],"study_design_scores_gemma":[0.00006018099,0.0001031461,0.3737669,0.001002352,0.0001570727,0.0001822505,0.00152819,0.005057848,0.0008920316,0.003556993,0.6135697,0.0001233774],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03663411,0.00396906,0.04143272,0.00670107,0.001871481,0.004635759,0.7203433,0.002099736,0.1823128],"genre_scores_gemma":[0.3021037,0.01054739,0.1154462,0.004393159,0.0009023569,0.00616192,0.4099228,0.001246281,0.1492762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9794827,"threshold_uncertainty_score":0.148864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2473435902851748,"score_gpt":0.3864117646181133,"score_spread":0.1390681743329385,"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."}}