{"id":"W6907797903","doi":"10.25318/9810059401-eng","title":"Labour force status by occupation minor group, industry sectors, age and gender: Canada, provinces and territories, census metropolitan areas and census agglomerations with parts","year":2023,"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; Minor (academic); American Community Survey; 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":[0.0007767629,0.001448949,0.001380969,0.004054397,0.001939082,0.002193547,0.003276281,0.001124694,0.04126449],"category_scores_gemma":[0.006249658,0.0009370971,0.001080255,0.01589928,0.00044554,0.0009118944,0.001264508,0.002025737,0.02140797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01524597,"about_ca_system_score_gemma":0.03601681,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.969374,"about_ca_topic_score_gemma":0.9763366,"domain_scores_codex":[0.9988153,0.00006293395,0.0001237847,0.0001869463,0.0004001468,0.0004108101],"domain_scores_gemma":[0.9943779,0.0002855453,0.0003864513,0.000278911,0.004004456,0.0006666542],"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":[0.00003858768,0.00001781159,0.003877681,0.0002193969,0.00002367731,0.00001536011,0.00004374383,0.0001603373,0.00001982501,0.0003407729,0.9937825,0.00146032],"study_design_scores_gemma":[0.0003329624,0.00001830645,0.1276089,0.0009141802,0.00007031139,0.0000823208,0.0006479993,0.0006915922,0.0003175274,0.0007005665,0.8685446,0.00007063716],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002413501,0.00003045014,0.00002196232,0.00003434225,0.00001070092,0.00001659934,0.9990695,0.00002351264,0.0005516393],"genre_scores_gemma":[0.001646968,0.00009081935,0.0001978129,0.00006555436,0.000007159733,0.0001447883,0.9951676,0.00002400241,0.002655398],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04126449,"threshold_uncertainty_score":0.1380434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01031661927640068,"score_gpt":0.258746902765221,"score_spread":0.2484302834888203,"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."}}