{"id":"W6976724365","doi":"10.6068/dp14ba7fc579f18","title":"Trend 1972 - 2013. Statistics Canada. CANSIM: Population and Demography - Mobility and Migration | Country: Canada | Table: Interprovincial migrants, by age group and sex | Variable: 68 years, Net-migration, Females | 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":[0.002063362,0.002282082,0.002531945,0.007320813,0.003108233,0.004522243,0.004960637,0.001247263,0.1131683],"category_scores_gemma":[0.01631003,0.001565451,0.001887994,0.03609389,0.0006098857,0.00230372,0.002452953,0.002966431,0.05855531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04592412,"about_ca_system_score_gemma":0.1191079,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.994416,"about_ca_topic_score_gemma":0.9921216,"domain_scores_codex":[0.9967218,0.0002517432,0.0003728235,0.0004340993,0.001474995,0.0007445531],"domain_scores_gemma":[0.9742125,0.0008921151,0.0006796459,0.0007767835,0.02202437,0.00141466],"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":[0.00002027663,0.000005728231,0.0009311717,0.000223977,0.00001902494,0.000006864091,0.00002821434,0.0001199838,0.00000890381,0.0003713725,0.996015,0.002249572],"study_design_scores_gemma":[0.0001673648,0.00001265266,0.0224682,0.0009051371,0.00006147719,0.00003183326,0.0005121209,0.0006002279,0.0001571977,0.0008194681,0.9741751,0.00008926122],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005835544,0.00005586367,0.00004601711,0.0001343623,0.00003699346,0.0000239888,0.9983208,0.00009102894,0.001232522],"genre_scores_gemma":[0.001182599,0.0003796146,0.0007079253,0.0001968459,0.0000252001,0.0002160028,0.9915355,0.0001930856,0.005563129],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1131683,"threshold_uncertainty_score":0.3785858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01761874772977056,"score_gpt":0.2352567446165272,"score_spread":0.2176379968867567,"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."}}