{"id":"W7095836986","doi":"","title":"Trends and Conditions in Census Metropolitan Areas Immigrants in Canada’s Census Metropolitan Areas","year":2015,"lang":"en","type":"article","venue":"","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Metropolitan area; Immigration; American Community Survey; Range (aeronautics)","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":[0.0003065679,0.0002112951,0.0002432795,0.003426017,0.001123317,0.0006963202,0.0007356553,0.0002515326,0.005821797],"category_scores_gemma":[0.001619716,0.0001942172,0.0004202582,0.007508929,0.000249985,0.0005086705,0.0004102797,0.0005738157,0.0008877699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00844292,"about_ca_system_score_gemma":0.01477053,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9881312,"about_ca_topic_score_gemma":0.9933071,"domain_scores_codex":[0.9995527,0.00002441642,0.00004983672,0.00005525383,0.0001700204,0.0001477967],"domain_scores_gemma":[0.9983114,0.00006835318,0.0002815376,0.00003426162,0.0009919305,0.0003124357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000101576,0.00005666293,0.9169295,0.0002201022,0.0001176734,0.00007769176,0.0008225053,0.0003538387,0.0001759265,0.0006823349,0.06638317,0.01407898],"study_design_scores_gemma":[0.000004403498,0.000009281646,0.9908728,0.00006456641,0.00002092132,0.00003051219,0.00118283,0.0003195161,0.0000390344,0.00003576655,0.007411653,0.000008744365],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6861525,0.004045995,0.0002938575,0.001427302,0.0001445013,0.0001498637,0.2884429,0.0001424705,0.0192006],"genre_scores_gemma":[0.8721958,0.005557488,0.0005587621,0.000269746,0.00006078069,0.0001748478,0.1080051,0.00004290619,0.01313442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01186883,"threshold_uncertainty_score":0.06125796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06399851219161204,"score_gpt":0.3310872012110378,"score_spread":0.2670886890194257,"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."}}