{"id":"W6901533109","doi":"10.6068/dp14ba7f7b36b82","title":"Trend 1972 - 2013. Statistics Canada. CANSIM: Population and Demography - Mobility and Migration | Country: Canada | Table: Interprovincial migrants, by age group and sex | Variable: 30 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; 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":[0.002062269,0.002277321,0.002547606,0.007308321,0.00312503,0.004537792,0.004929936,0.00124771,0.110174],"category_scores_gemma":[0.01611247,0.001574064,0.001890085,0.03628439,0.0006139231,0.002291435,0.002456717,0.002961071,0.05702415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04665066,"about_ca_system_score_gemma":0.1208421,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9946277,"about_ca_topic_score_gemma":0.9924237,"domain_scores_codex":[0.9966968,0.0002511111,0.0003756369,0.000434225,0.001486313,0.0007559165],"domain_scores_gemma":[0.9743464,0.0008803329,0.0006835073,0.0007616829,0.0218982,0.001429828],"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":[0.00002070551,0.000005849025,0.0009650469,0.0002286014,0.00001933335,0.000007009646,0.0000293599,0.0001172069,0.000009008335,0.0003675577,0.9960139,0.002216476],"study_design_scores_gemma":[0.0001711883,0.00001318026,0.02423357,0.0009255597,0.00006355242,0.00003275485,0.0005466263,0.0005943288,0.0001621145,0.0007984601,0.9723675,0.00009099654],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006017249,0.00005726617,0.00004382763,0.0001351268,0.00003729324,0.00002405352,0.9983408,0.00008779979,0.001213663],"genre_scores_gemma":[0.001215623,0.0003867014,0.0006859602,0.000200251,0.00002547521,0.0002152699,0.9914518,0.0001865351,0.005632413],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.110174,"threshold_uncertainty_score":0.3685688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01737111801918684,"score_gpt":0.2351170171295115,"score_spread":0.2177458991103247,"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."}}