{"id":"W6950753133","doi":"10.5683/sp2/7wr7fg","title":"2016 Census of Canada - Selected Characteristics for Housing - Vancouver, Toronto, Montreal CMAs at the Census Tract (CT) Level [custom tabulation] 001","year":2019,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Census tract; Bedroom; Subsidy; Table (database); Order (exchange); Household income","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":[0.0007761787,0.001243084,0.001344461,0.00513164,0.001981339,0.002418146,0.001966345,0.000474247,0.05599249],"category_scores_gemma":[0.005544879,0.000691979,0.0009605957,0.02034299,0.0003648907,0.0008865361,0.001153461,0.001363166,0.02154064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03151789,"about_ca_system_score_gemma":0.07492875,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9921761,"about_ca_topic_score_gemma":0.9951054,"domain_scores_codex":[0.9985935,0.0000729079,0.0001138838,0.0002019766,0.000727599,0.0002900431],"domain_scores_gemma":[0.9920334,0.0001833045,0.0002872578,0.0002733945,0.006589763,0.0006328541],"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":[0.00002021223,0.000007793796,0.00633004,0.0002488777,0.00002255749,0.00001756183,0.0001043224,0.0001527635,0.00003175328,0.000699178,0.9890397,0.003325301],"study_design_scores_gemma":[0.00006196275,0.00001248928,0.1658132,0.0006592643,0.00004907597,0.00005975602,0.001084977,0.0006026439,0.0001950638,0.0004631184,0.8309385,0.00006001966],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007110905,0.0001127458,0.00007339624,0.00009908458,0.0000337925,0.00003977361,0.9957559,0.00005998303,0.003114332],"genre_scores_gemma":[0.006445291,0.0005711057,0.0008035213,0.0001107503,0.00002425227,0.0002162682,0.9806044,0.0001062965,0.01111806],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05599249,"threshold_uncertainty_score":0.2286794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02749317480410477,"score_gpt":0.2560648537570924,"score_spread":0.2285716789529876,"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."}}