{"id":"W6976899471","doi":"10.6068/dp14ba7fdf5d158","title":"Trend 1972 - 2013. Statistics Canada. CANSIM: Population and Demography - Mobility and Migration | Country: Canada | Table: Interprovincial migrants, by age group and sex | Variable: 45 years, In-migrants, 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":[0.002100202,0.002242805,0.002541787,0.007176087,0.003116698,0.004521777,0.004899881,0.001247982,0.114288],"category_scores_gemma":[0.01707276,0.00156506,0.00186437,0.03609774,0.0006117527,0.002285896,0.002494488,0.002915027,0.05907132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04510794,"about_ca_system_score_gemma":0.1183819,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9942326,"about_ca_topic_score_gemma":0.9920326,"domain_scores_codex":[0.9967092,0.0002590528,0.0003800374,0.000436596,0.001464595,0.0007504447],"domain_scores_gemma":[0.97407,0.0009244891,0.0006825686,0.0008019658,0.02210335,0.001417715],"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":[0.0000203469,0.00000553006,0.0009014942,0.0002253048,0.00001862404,0.000006840366,0.00002929835,0.0001137342,0.000008734673,0.0003632169,0.9961419,0.002164959],"study_design_scores_gemma":[0.000169271,0.00001260598,0.02228609,0.000928966,0.0000612051,0.00003202216,0.0005395905,0.0005769718,0.0001548901,0.0008248751,0.9743237,0.00008970626],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005627171,0.0000548127,0.00004545932,0.0001339971,0.00003642994,0.00002336288,0.9983733,0.00008916322,0.00118718],"genre_scores_gemma":[0.001175302,0.0003772358,0.0007048247,0.0002000546,0.0000253704,0.0002178797,0.9917476,0.0001932344,0.005358501],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.114288,"threshold_uncertainty_score":0.3823316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01375900889915707,"score_gpt":0.2300275567343499,"score_spread":0.2162685478351929,"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."}}