{"id":"W6907506431","doi":"10.25318/1410018101-eng","title":"Employment Insurance program (EI), beneficiaries by province, census metropolitan area, census agglomeration, total and regular income benefits, declared earnings, sex and age","year":2020,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Beneficiary; Metropolitan area; Table (database); Data collection; Demographic analysis; Race (biology)","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":[0.0007507961,0.001012234,0.001145009,0.004397974,0.001039129,0.001837176,0.001819915,0.0006865639,0.07071743],"category_scores_gemma":[0.006715008,0.0006801433,0.0007154227,0.01465124,0.0002825934,0.0009779158,0.000945506,0.001319071,0.04265149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009925561,"about_ca_system_score_gemma":0.02032289,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8835507,"about_ca_topic_score_gemma":0.8941424,"domain_scores_codex":[0.9989426,0.00005707717,0.0001206587,0.0001906849,0.0004468674,0.0002421521],"domain_scores_gemma":[0.9945239,0.0004405715,0.0004241379,0.0003283933,0.00380136,0.0004815276],"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":[0.00001835291,0.000007461569,0.002005631,0.0001667239,0.00001044576,0.000006616147,0.0000148344,0.0001112972,0.00001340599,0.000354959,0.9955215,0.001768722],"study_design_scores_gemma":[0.0001028335,0.00001103581,0.03850285,0.0004910758,0.00003421034,0.00003197826,0.00020708,0.0003704809,0.0001511362,0.0004270239,0.9596413,0.00002895634],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001158254,0.00002384039,0.00001545103,0.00003704101,0.000008008403,0.000007410214,0.998996,0.00002826711,0.0007681706],"genre_scores_gemma":[0.0009658213,0.0001093235,0.0001424499,0.00005104176,0.000006305614,0.00005681345,0.9958254,0.00002794676,0.002814848],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1164493,"threshold_uncertainty_score":0.2365733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007912117360203245,"score_gpt":0.2472797707909193,"score_spread":0.2393676534307161,"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."}}