{"id":"W6931693286","doi":"10.5683/sp3/dp7p2b","title":"Census of Population, 2016 [Canada]: Data Tables, Mobility and Migration [B2020]","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"Marine Bivalve and Aquaculture Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Aggregate data; Population; Aggregate (composite); Internal migration; Table (database)","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":[0.000799382,0.001803685,0.001474125,0.004777158,0.001386564,0.002553012,0.002654849,0.001196014,0.04029274],"category_scores_gemma":[0.007539127,0.000821236,0.001071766,0.02109431,0.0004485999,0.0009701401,0.00121103,0.001958322,0.03423934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01023916,"about_ca_system_score_gemma":0.02714965,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9093766,"about_ca_topic_score_gemma":0.9350272,"domain_scores_codex":[0.9990147,0.00007555152,0.0001269028,0.0001886674,0.0003713538,0.0002226992],"domain_scores_gemma":[0.9947129,0.0004888813,0.0002983756,0.0004192778,0.003566211,0.0005143651],"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":[0.00002243348,0.00000727205,0.001030909,0.0002266851,0.00001685006,0.00000901511,0.00001477958,0.0002097679,0.00001719631,0.0002351393,0.9971366,0.001073358],"study_design_scores_gemma":[0.0001710325,0.000008948854,0.02293207,0.0004553347,0.00004174377,0.00003805539,0.000169471,0.0008439416,0.0001979587,0.0007850431,0.9742982,0.00005823355],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000506237,0.00002054693,0.00001309548,0.00002447255,0.00000728159,0.000003967281,0.9996023,0.00004230882,0.0002355242],"genre_scores_gemma":[0.0003315077,0.00005747285,0.0001023431,0.0000229479,0.000002870996,0.00002909132,0.9989625,0.00002214707,0.000469127],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09062344,"threshold_uncertainty_score":0.1823142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01796790793543054,"score_gpt":0.2494395978367603,"score_spread":0.2314716899013298,"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."}}