{"id":"W6958050629","doi":"10.6068/dp14ba7f78b6167","title":"Trend 1972 - 2013. Statistics Canada. CANSIM: Population and Demography - Mobility and Migration | Country: Canada | Table: Interprovincial migrants, by age group and sex | Variable: 29 years, Out-migrants, Males | Units: # Persons, 1972-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-160.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Official statistics; Population; Population statistics; Summary statistics; Demographic statistics; Economic statistics; Socioeconomic status; Internal migration","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":[0.002065684,0.002276336,0.002545886,0.007296997,0.003139612,0.004544155,0.004941461,0.001247341,0.1111749],"category_scores_gemma":[0.01607124,0.001574715,0.001886632,0.03614238,0.0006142512,0.002299031,0.002464813,0.002966147,0.05755677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0465808,"about_ca_system_score_gemma":0.1208497,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9946074,"about_ca_topic_score_gemma":0.9924136,"domain_scores_codex":[0.996694,0.0002517482,0.000375098,0.0004347351,0.001486895,0.000757583],"domain_scores_gemma":[0.9743314,0.0008803831,0.000682537,0.0007631698,0.02190755,0.00143503],"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":[0.00002066689,0.000005844878,0.000959738,0.0002269719,0.0000191289,0.000006973249,0.00002944186,0.0001166062,0.000009012135,0.0003677138,0.9960226,0.002215322],"study_design_scores_gemma":[0.0001701085,0.00001318281,0.02404846,0.0009236136,0.0000627264,0.00003252791,0.0005493689,0.0005921045,0.0001613667,0.0007982376,0.9725575,0.00009091819],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006032725,0.00005706953,0.00004418483,0.000135911,0.00003754079,0.00002425511,0.9983212,0.00008849494,0.001231034],"genre_scores_gemma":[0.001218086,0.0003872853,0.0006912755,0.0002015746,0.00002555568,0.0002171418,0.9913646,0.0001888952,0.005705716],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1111749,"threshold_uncertainty_score":0.3719171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01733988852679343,"score_gpt":0.2350291786497069,"score_spread":0.2176892901229134,"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."}}