{"id":"W6939223088","doi":"10.6068/dp14ba8f4e85491","title":"Trend 1961 - 2010. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Hours worked and labour compensation by type of worker and North American Industry Classification System (NAICS) | Variable: 15 to 34 years, University degrees or above, Labour compensation, Paid workers, Postal service and couriers and messengers, Females | Units: , 1961-2010. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-145.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Earnings; Census; Wages and salaries; Economic statistics; Summary statistics; Socioeconomic status; Official statistics; Immigration; Compensation of employees","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":[0.001727894,0.002520907,0.002694789,0.007959601,0.00288762,0.004399944,0.00514185,0.001479381,0.07530173],"category_scores_gemma":[0.01424816,0.001606737,0.001875978,0.03855634,0.0006088246,0.002187678,0.001954373,0.003199179,0.0546435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04367693,"about_ca_system_score_gemma":0.1026046,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9932311,"about_ca_topic_score_gemma":0.9916577,"domain_scores_codex":[0.9963658,0.0002022407,0.0003511457,0.0005202515,0.001634903,0.0009255958],"domain_scores_gemma":[0.9718851,0.0009546229,0.001034727,0.0007800602,0.0240509,0.001294535],"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":[0.00002631228,0.000007891629,0.001257622,0.0001846413,0.00002037712,0.00000584775,0.00001758632,0.0001127517,0.000009624407,0.000255329,0.9968067,0.001295377],"study_design_scores_gemma":[0.0002200274,0.00001578144,0.03706996,0.0007934091,0.00006819244,0.00002702085,0.0004847167,0.0005515161,0.0002412241,0.0005997897,0.9598308,0.00009771452],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006305108,0.00003979996,0.00001648751,0.00008260101,0.00002257226,0.00001013115,0.9991561,0.00004815074,0.0005610968],"genre_scores_gemma":[0.0006700066,0.0001682669,0.0002049472,0.00009666711,0.0000158037,0.00007074071,0.9956785,0.00006691914,0.003028183],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07530173,"threshold_uncertainty_score":0.3168998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03236322377262767,"score_gpt":0.2384218110595932,"score_spread":0.2060585872869655,"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."}}