{"id":"W6920128736","doi":"10.6068/dp14ba80199308","title":"Trend 1972 - 2013. Statistics Canada. CANSIM: Population and Demography - Mobility and Migration | Country: Canada | Table: Interprovincial migrants, by age group and sex | Variable: 25 years, Net-migration, Both sexes | 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; Demographic statistics; Summary 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":[0.002073411,0.002243125,0.002511263,0.007162266,0.00306316,0.004490087,0.004892488,0.001235194,0.1147467],"category_scores_gemma":[0.01680538,0.001550109,0.001866069,0.03582609,0.0006081223,0.002276964,0.002450434,0.002906037,0.0591225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04502169,"about_ca_system_score_gemma":0.1172764,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9941844,"about_ca_topic_score_gemma":0.9919285,"domain_scores_codex":[0.9967583,0.0002529755,0.0003705324,0.0004324075,0.001448912,0.0007368965],"domain_scores_gemma":[0.9743197,0.0009083497,0.0006742587,0.0007962603,0.02191332,0.001388158],"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":[0.0000200558,0.000005568227,0.0009017432,0.0002238764,0.00001879112,0.000006844955,0.00002846557,0.0001190092,0.000008823225,0.0003703709,0.9960973,0.002199244],"study_design_scores_gemma":[0.0001655548,0.00001226809,0.02152017,0.000895882,0.00006026973,0.00003155681,0.0005105369,0.0005872658,0.0001552869,0.0008330173,0.97514,0.00008826819],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000561602,0.00005396858,0.00004638639,0.0001307069,0.00003540919,0.00002281931,0.9983771,0.00009075028,0.001186637],"genre_scores_gemma":[0.001169281,0.0003705983,0.0007201377,0.0001940386,0.00002467186,0.0002126479,0.9917721,0.0001960657,0.005340464],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1147467,"threshold_uncertainty_score":0.383866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01322824013297827,"score_gpt":0.2278921488808302,"score_spread":0.2146639087478519,"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."}}