{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002791845,0.002581129,0.003046992,0.00798575,0.003428797,0.00472335,0.005747173,0.001455193,0.08503113],"category_scores_gemma":[0.01884553,0.002037077,0.002554266,0.03782202,0.0006144061,0.002314285,0.002482454,0.003384115,0.04521833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05609627,"about_ca_system_score_gemma":0.1542674,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9958394,"about_ca_topic_score_gemma":0.9943513,"domain_scores_codex":[0.9951621,0.0003505376,0.0005570559,0.0005609418,0.002239159,0.001130142],"domain_scores_gemma":[0.9619709,0.001186741,0.001117669,0.001005212,0.03284027,0.001879307],"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":[0.00003669217,0.00001010153,0.001627231,0.0002930964,0.00003030748,0.000008018722,0.00003037386,0.000111179,0.00001195822,0.0003410228,0.9952447,0.002255285],"study_design_scores_gemma":[0.0002547154,0.00002300184,0.04793492,0.001370646,0.0001195582,0.00003926625,0.000718055,0.0005888662,0.000229541,0.0008035034,0.947793,0.0001249857],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008537831,0.00006685984,0.00003209531,0.00015717,0.00004359783,0.00002620395,0.9985098,0.00006254064,0.001016262],"genre_scores_gemma":[0.001215834,0.000396317,0.0005905707,0.0002715964,0.00002914511,0.0002205241,0.9906218,0.0001476047,0.006506601],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08503113,"threshold_uncertainty_score":0.4070088,"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."}}