{"id":"W6957586435","doi":"10.6068/dp14ba8880dfd4","title":"Trend 2006 - 2013. Statistics Canada. CANSIM: Ethnic Diversity and Immigration - Immigrants and Nonpermanent Residents | Country: Canada | Table: Labour force survey estimates (LFS), by immigrant status, country of birth, sex and age group | Variable: 15 years and over, Asia, Employment, Females, Landed immigrants | Units: , 2006-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-092.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Census; Socioeconomic status; Population; Official statistics; Population statistics; Ethnic group; Diversity (politics); American Community Survey","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":[0.0008075105,0.0006029346,0.0009008616,0.0001132653,0.0003098496,0.0003128613,0.0005772903,0.0003634116,0.0006767028],"category_scores_gemma":[0.00006532795,0.0006023801,2.693178e-7,0.000253921,0.0002713462,0.0004974437,0.0008191066,0.0003183276,0.000003299567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001175317,"about_ca_system_score_gemma":0.001494615,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.999882,"about_ca_topic_score_gemma":0.9997726,"domain_scores_codex":[0.996133,0.0004235073,0.0007472375,0.00106573,0.000997021,0.0006335165],"domain_scores_gemma":[0.9972184,0.0004147358,0.0008040574,0.0009760346,0.00008015407,0.0005066183],"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":[0.0002506646,0.00006580316,0.003633437,0.0005746523,0.0001239942,0.0001661403,0.00002580516,0.000002047405,0.0006331921,0.00002051452,0.9944193,0.00008445893],"study_design_scores_gemma":[0.001366997,0.00008516204,0.01400097,0.00006854441,0.0002507707,0.00007776939,0.0002954151,0.0007384887,7.277348e-7,2.893223e-7,0.9824829,0.0006319339],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002994908,0.005737188,0.00001068533,0.000002214809,0.0003938952,0.000591748,0.9902024,0.00004017574,0.00002677632],"genre_scores_gemma":[0.00152733,0.01183278,0.00005336592,0.0001145421,0.00004831744,0.000007950798,0.983857,0.0001005518,0.002458149],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01193636,"threshold_uncertainty_score":0.9996427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02316027143751658,"score_gpt":0.247836295668484,"score_spread":0.2246760242309674,"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."}}