{"id":"W6976919141","doi":"10.6068/dp14ba7f673ad58","title":"Trend 1972 - 2013. Statistics Canada. CANSIM: Population and Demography - Mobility and Migration | Country: Canada | Table: Interprovincial migrants, by age group and sex | Variable: 84 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":[0.00206148,0.002289792,0.002563913,0.007305215,0.003136693,0.0045413,0.004950825,0.001257154,0.1120092],"category_scores_gemma":[0.01620493,0.001579729,0.001890732,0.03624784,0.0006116513,0.002306549,0.002480677,0.002973452,0.0580416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04599656,"about_ca_system_score_gemma":0.1198509,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9944959,"about_ca_topic_score_gemma":0.992253,"domain_scores_codex":[0.9967074,0.0002530818,0.0003765112,0.0004339813,0.001476142,0.000752793],"domain_scores_gemma":[0.9743797,0.0008848881,0.0006841795,0.0007664782,0.02185084,0.001433998],"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":[0.00002079617,0.000005774729,0.0009499877,0.0002279912,0.00001915088,0.000006902303,0.00002895201,0.0001151352,0.000008892907,0.0003638962,0.9960256,0.002226988],"study_design_scores_gemma":[0.0001730277,0.00001329468,0.02394135,0.0009390065,0.00006349177,0.00003268747,0.0005425941,0.0005957565,0.0001595217,0.0008116548,0.9726363,0.00009122991],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005938676,0.00005751336,0.00004433481,0.0001359839,0.00003766182,0.00002429034,0.9983338,0.00008846188,0.001218632],"genre_scores_gemma":[0.001204595,0.0003906546,0.0006876478,0.00020264,0.0000259377,0.000219168,0.9914551,0.0001890115,0.005625326],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1120092,"threshold_uncertainty_score":0.374708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01514941219429298,"score_gpt":0.2311324815339555,"score_spread":0.2159830693396625,"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."}}