{"id":"W6901542790","doi":"10.6068/dp14ba801a37c12","title":"Trend 1972 - 2013. Statistics Canada. CANSIM: Population and Demography - Mobility and Migration | Country: Canada | Table: Interprovincial migrants, by age group and sex | Variable: 70 to 74 years, Out-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; Economic statistics; Demographic 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":[0.002180437,0.002285006,0.002545577,0.007327307,0.003100568,0.004486993,0.004915261,0.001251001,0.1132225],"category_scores_gemma":[0.0169734,0.001623875,0.001959187,0.0363709,0.0006128895,0.002308525,0.002451752,0.002978913,0.0574448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04688359,"about_ca_system_score_gemma":0.1222291,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.994525,"about_ca_topic_score_gemma":0.9922352,"domain_scores_codex":[0.9965713,0.0002646572,0.0003959931,0.0004415206,0.001544617,0.0007818756],"domain_scores_gemma":[0.9727036,0.0009407077,0.0007087479,0.000806015,0.02337887,0.00146201],"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":[0.00002252694,0.000006150751,0.00101329,0.0002434941,0.00002020432,0.00000730973,0.000030395,0.0001190134,0.000009366217,0.0003607479,0.9958348,0.00233261],"study_design_scores_gemma":[0.000177117,0.00001407901,0.0257058,0.0009749955,0.00006615437,0.00003418311,0.000571208,0.0005874354,0.0001628123,0.0008208759,0.9707913,0.00009398065],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006077326,0.00005764173,0.00004445504,0.0001363311,0.00003784529,0.000025036,0.9983374,0.00008894547,0.001211416],"genre_scores_gemma":[0.001263237,0.0003978456,0.0007318098,0.0002096865,0.00002640819,0.0002305026,0.9910958,0.0001980227,0.005846678],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1132225,"threshold_uncertainty_score":0.378767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01748517317445736,"score_gpt":0.2382822934148721,"score_spread":0.2207971202404148,"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."}}