{"id":"W6901472599","doi":"10.6068/dp14ba7f67f8f63","title":"Trend 1972 - 2013. Statistics Canada. CANSIM: Population and Demography - Mobility and Migration | Country: Canada | Table: Interprovincial migrants, by age group and sex | Variable: 79 years, In-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; 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":[],"consensus_categories":[],"category_scores_codex":[0.002055307,0.002285641,0.00255816,0.007302623,0.003134717,0.004528331,0.004956146,0.001253404,0.1111076],"category_scores_gemma":[0.01610724,0.001572256,0.00189222,0.03616397,0.0006120664,0.002304113,0.002474742,0.002969671,0.05743855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04608081,"about_ca_system_score_gemma":0.1199265,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.994511,"about_ca_topic_score_gemma":0.9922779,"domain_scores_codex":[0.9967163,0.0002515746,0.0003753474,0.000432796,0.001471802,0.0007521797],"domain_scores_gemma":[0.9744785,0.0008763574,0.0006808975,0.000760888,0.02177725,0.001426195],"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":[0.0000208671,0.000005786874,0.0009576741,0.0002283008,0.0000192348,0.00000695415,0.00002909447,0.0001149957,0.000008923993,0.0003652352,0.9960303,0.002212591],"study_design_scores_gemma":[0.0001736334,0.00001331667,0.02413619,0.0009401422,0.00006375791,0.00003300249,0.000548806,0.0005960395,0.0001604899,0.0008099587,0.9724333,0.00009142955],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005946058,0.0000571679,0.00004370872,0.0001355252,0.00003751115,0.00002414856,0.998343,0.0000873369,0.001212206],"genre_scores_gemma":[0.001205025,0.0003877826,0.0006799381,0.0002008738,0.00002577268,0.0002168637,0.9915472,0.0001861455,0.005550484],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1111076,"threshold_uncertainty_score":0.3716919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01513686604686098,"score_gpt":0.2311147680463117,"score_spread":0.2159779019994507,"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."}}