{"id":"W6976459118","doi":"10.6068/dp14ba7f8f6154","title":"Trend 1972 - 2013. Statistics Canada. CANSIM: Population and Demography - Mobility and Migration | Country: Canada | Table: Interprovincial migrants, by age group and sex | Variable: 58 years, Net-migration, 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; Socioeconomic status; Economic statistics; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002052936,0.002284811,0.002539215,0.007310058,0.003110018,0.00452304,0.004976772,0.001245409,0.1130842],"category_scores_gemma":[0.01612768,0.001567729,0.001894714,0.03601002,0.0006116133,0.002304086,0.002452592,0.002974085,0.05836544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04601869,"about_ca_system_score_gemma":0.1192982,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9944704,"about_ca_topic_score_gemma":0.9921967,"domain_scores_codex":[0.9967269,0.000250785,0.0003703967,0.000434524,0.001472081,0.000745383],"domain_scores_gemma":[0.9744312,0.0008799238,0.0006769032,0.000769884,0.02182859,0.001413552],"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":[0.00002027977,0.000005780224,0.0009441295,0.0002247509,0.00001919644,0.000006873003,0.00002834564,0.0001199232,0.000008914214,0.0003723974,0.9960002,0.002249077],"study_design_scores_gemma":[0.0001688113,0.00001283466,0.02292274,0.0009072221,0.00006229368,0.00003212472,0.0005173696,0.0006057603,0.0001585988,0.0008223453,0.9737002,0.00008975295],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005915576,0.00005627584,0.00004592229,0.000134168,0.00003701834,0.00002398377,0.9983218,0.00009092639,0.001230618],"genre_scores_gemma":[0.00119133,0.0003799583,0.000706301,0.0001972896,0.00002528066,0.0002150703,0.9915003,0.0001925607,0.005591807],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8869158,"threshold_uncertainty_score":0.3783043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01460301565695989,"score_gpt":0.2289715443956255,"score_spread":0.2143685287386656,"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."}}