{"id":"W6901594050","doi":"10.6068/dp14ba801a88b13","title":"Trend 1972 - 2013. Statistics Canada. CANSIM: Population and Demography - Mobility and Migration | Country: Canada | Table: Interprovincial migrants, by age group and sex | Variable: 86 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":[0.002051354,0.002240241,0.002499948,0.007137566,0.003059978,0.004461572,0.004879841,0.001235067,0.1145516],"category_scores_gemma":[0.01661967,0.001548137,0.001860679,0.03547951,0.0006046428,0.002263641,0.002448983,0.002893252,0.05895479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04459427,"about_ca_system_score_gemma":0.1163067,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9941821,"about_ca_topic_score_gemma":0.9919852,"domain_scores_codex":[0.9968017,0.0002482569,0.0003642339,0.0004267948,0.001429174,0.0007298291],"domain_scores_gemma":[0.9746328,0.0008935896,0.0006683598,0.0007858697,0.02163621,0.001383086],"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":[0.00002013858,0.000005585613,0.0009103249,0.0002233175,0.00001875687,0.000006830882,0.00002834294,0.0001190409,0.000008843849,0.0003651993,0.9960991,0.002194511],"study_design_scores_gemma":[0.0001681948,0.00001251135,0.02207825,0.0009059423,0.00006077593,0.00003189548,0.0005151326,0.0005993006,0.0001570611,0.0008347002,0.9745472,0.00008904666],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005695094,0.00005394779,0.00004640409,0.0001310248,0.00003555924,0.00002287362,0.998378,0.00009060932,0.001184532],"genre_scores_gemma":[0.001181676,0.0003701818,0.0007148318,0.0001944078,0.00002493159,0.000212568,0.9917457,0.0001948994,0.005360921],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1145516,"threshold_uncertainty_score":0.3832132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01335430010498005,"score_gpt":0.2278466346283792,"score_spread":0.2144923345233992,"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."}}