{"id":"W6920240164","doi":"10.6068/dp14ba7ffeceb48","title":"Trend 2002 - 2013. Statistics Canada. CANSIM: Population and Demography - Mobility and Migration | Country: Canada | Table: Interprovincial migrants, by age group and sex | Variable: 95 years, Out-migrants, Both sexes | Units: # Persons, 2002-2013. 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":[0.002106318,0.002265034,0.002562064,0.007047206,0.003305563,0.004569954,0.004972306,0.001231229,0.10908],"category_scores_gemma":[0.01641159,0.001622667,0.001993175,0.03516747,0.000594835,0.002239353,0.002416578,0.002906373,0.05226767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04890263,"about_ca_system_score_gemma":0.1257059,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9955438,"about_ca_topic_score_gemma":0.9936558,"domain_scores_codex":[0.9967793,0.0002615209,0.0003718893,0.0004179674,0.001424376,0.0007449951],"domain_scores_gemma":[0.9733163,0.0008847691,0.0006715093,0.0007734001,0.02289977,0.001454237],"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":[0.00002440157,0.000006143344,0.001086535,0.0002563598,0.00002221047,0.000007635984,0.00003541088,0.0001255858,0.000009976863,0.0004356797,0.9953777,0.002612438],"study_design_scores_gemma":[0.0001865862,0.00001493983,0.02721927,0.001003434,0.00007737688,0.00003768179,0.0006384181,0.0007146082,0.0001725894,0.0009004868,0.9689312,0.0001035522],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007392349,0.00007145237,0.00005630977,0.0001676255,0.00004330013,0.00002864725,0.9979706,0.0001068487,0.001481259],"genre_scores_gemma":[0.001573182,0.0004865767,0.000911257,0.0002493156,0.00002808933,0.0002540882,0.9898317,0.0002295069,0.006436164],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.10908,"threshold_uncertainty_score":0.364909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01570460006367275,"score_gpt":0.2308584645912772,"score_spread":0.2151538645276044,"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."}}