{"id":"W6963980483","doi":"10.25318/9810010201-eng","title":"Low-income status by age, gender and year: Canada, provinces and territories, census metropolitan areas and census agglomerations with parts","year":2022,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Metropolitan area; Urban agglomeration; Measure (data warehouse); Population","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":[0.001157032,0.001943531,0.001912897,0.004893517,0.001750398,0.002853731,0.004522763,0.001415622,0.04699448],"category_scores_gemma":[0.008738686,0.001166897,0.001601509,0.01793433,0.0005110781,0.001314564,0.001675081,0.002490809,0.01953475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01433155,"about_ca_system_score_gemma":0.03191774,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9640181,"about_ca_topic_score_gemma":0.9745058,"domain_scores_codex":[0.9986071,0.00008274267,0.0001693781,0.0002508963,0.0004462153,0.0004437083],"domain_scores_gemma":[0.9922814,0.0004481701,0.0005940804,0.0004495054,0.005395994,0.0008308569],"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":[0.00004709486,0.00001444866,0.003133522,0.0003982723,0.00003815,0.0000141613,0.00002921126,0.0001616011,0.00001675452,0.000387377,0.9945009,0.001258607],"study_design_scores_gemma":[0.0005858276,0.00002252171,0.1189061,0.001644367,0.0001532182,0.000117168,0.0005621127,0.000822132,0.0002782892,0.001054668,0.8757579,0.00009576269],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001345992,0.00004000084,0.00001623687,0.00003726078,0.00001039472,0.00001016212,0.9993896,0.00002359365,0.000338116],"genre_scores_gemma":[0.001515712,0.0001197758,0.0001942094,0.00007822518,0.000008411667,0.0001055629,0.9961701,0.0000304097,0.001777732],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04699448,"threshold_uncertainty_score":0.1572122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006520148789969966,"score_gpt":0.2428972855579906,"score_spread":0.2363771367680207,"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."}}