{"id":"W6976471692","doi":"10.6068/dp14ba7ee9b168","title":"Trend 1971 - 2013. Statistics Canada. CANSIM: Population and Demography - Mobility and Migration | Country: Canada | Table: Number of non-permanent residents, by age group and sex for July 1 | Variable: 23 years, Males | Units: # Persons, 1971-2013. 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; Population; Official statistics; Population statistics; Summary statistics; Demographic statistics; Socioeconomic status; Economic statistics","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":[0.002020622,0.002224811,0.002540121,0.007108278,0.003054311,0.004364089,0.004918716,0.00122484,0.1095455],"category_scores_gemma":[0.01670638,0.001527412,0.001860821,0.03480782,0.0006017981,0.0022786,0.002388913,0.00295809,0.05923307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04483165,"about_ca_system_score_gemma":0.1164055,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9942158,"about_ca_topic_score_gemma":0.9920804,"domain_scores_codex":[0.9968296,0.0002420828,0.0003567214,0.0004128871,0.001431007,0.0007277966],"domain_scores_gemma":[0.973879,0.0008933001,0.0006603975,0.0007529321,0.02246218,0.001352275],"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":[0.00001952076,0.000005809336,0.000951639,0.0002221104,0.00001843376,0.000006724521,0.00002781947,0.0001202525,0.000008368675,0.0003549019,0.9960504,0.002214005],"study_design_scores_gemma":[0.0001676468,0.00001316186,0.0241271,0.0009211058,0.000061489,0.00003293677,0.0005263814,0.00062497,0.0001571456,0.0008108962,0.9724666,0.00009051634],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006252705,0.00005825735,0.00004536507,0.0001399615,0.00003772898,0.00002393971,0.9983349,0.00008963962,0.00120768],"genre_scores_gemma":[0.00124078,0.0004084211,0.0006782204,0.0001976011,0.00002628339,0.0002123133,0.9916414,0.0001883383,0.005406704],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1095455,"threshold_uncertainty_score":0.3664663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02102766461566033,"score_gpt":0.2597478918503446,"score_spread":0.2387202272346843,"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."}}