{"id":"W6945526003","doi":"10.25318/9810052401-eng","title":"Languages used at work by languages used at home, immigrant status and period of immigration: 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; Immigration; Population; Period (music)","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":[0.0007988437,0.00170724,0.001538001,0.004628321,0.001944051,0.002523287,0.003132707,0.001098091,0.02806062],"category_scores_gemma":[0.006020545,0.0009550465,0.001268778,0.01708693,0.000461592,0.00107048,0.001468978,0.001981009,0.01266979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01433675,"about_ca_system_score_gemma":0.03140052,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9742156,"about_ca_topic_score_gemma":0.9812698,"domain_scores_codex":[0.9988159,0.00006649931,0.0001564642,0.0002141028,0.0003628752,0.0003841747],"domain_scores_gemma":[0.9944963,0.0002962721,0.0004755642,0.0002631851,0.003811842,0.0006568505],"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":[0.00006949197,0.00002225902,0.009765774,0.0004730978,0.00005670785,0.00002388577,0.00008666249,0.0002202704,0.00003021164,0.0003668445,0.9870776,0.001807221],"study_design_scores_gemma":[0.0004716951,0.00002723353,0.2605236,0.001325753,0.0001413905,0.0001186672,0.00107288,0.0008060757,0.0003472222,0.0005943695,0.7344603,0.0001107735],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003785981,0.00004895752,0.00001843441,0.00003987996,0.00001074428,0.00001485785,0.9989967,0.00002427788,0.0004675525],"genre_scores_gemma":[0.002459736,0.0001304302,0.0001680294,0.00006092392,0.00000723218,0.0001355021,0.9952898,0.00002350908,0.001724896],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02806062,"threshold_uncertainty_score":0.104021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004261052750584312,"score_gpt":0.236058080876198,"score_spread":0.2317970281256137,"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."}}