{"id":"W6976612259","doi":"10.6068/dp14ba8d6b3f457","title":"Trend 2006 - 2013. Statistics Canada. CANSIM: Ethnic Diversity and Immigration - Labor Market and Income | Country: Canada | Table: Labour force survey estimates (LFS), by immigrant status, educational attainment, sex and age group | Variable: 15 years and over, Labour force, High school graduate, Both sexes, Immigrants, landed more than 10 years earlier | Units: , 2006-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-094.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Unemployment; Census; Official statistics; Socioeconomic status; Descriptive statistics; Population; Ethnic group; Diversity (politics); Government (linguistics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000679334,0.000665263,0.000822229,0.0001230702,0.0002396489,0.0002871887,0.0005687056,0.0003674313,0.0006091429],"category_scores_gemma":[0.00005824002,0.0006715652,3.253762e-7,0.0003082909,0.0001914179,0.0003648813,0.0005773419,0.0004966163,0.000002381311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001812586,"about_ca_system_score_gemma":0.00143323,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.999825,"about_ca_topic_score_gemma":0.9990917,"domain_scores_codex":[0.996747,0.0002428525,0.0005681968,0.0009043373,0.0007879001,0.0007496998],"domain_scores_gemma":[0.9975707,0.0004560592,0.0003040911,0.0009586373,0.00004714775,0.0006633539],"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":[0.00006468001,0.00002938713,0.003011852,0.0005689483,0.0002644959,0.00009230716,0.0000164936,0.00007931593,0.000005379493,0.00009214948,0.995702,0.0000729692],"study_design_scores_gemma":[0.001067116,0.00005503945,0.01986404,0.00005953085,0.0001614922,0.00004681597,0.0002529406,0.00467301,1.421514e-8,8.355311e-7,0.9730545,0.0007647019],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001200786,0.01135655,0.0000105018,0.000002867404,0.0003683377,0.0004414282,0.9864782,0.00006026398,0.0000810483],"genre_scores_gemma":[0.001139021,0.008141379,0.0001334519,0.00007253992,0.00009700689,0.00001504802,0.9803614,0.0001612225,0.009878931],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02264756,"threshold_uncertainty_score":0.9995735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01245143950833544,"score_gpt":0.2191970162314547,"score_spread":0.2067455767231193,"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."}}