{"id":"W6919973191","doi":"10.6068/dp14ba8e0e2fc29","title":"Trend 1997 - 2013. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Labour force survey estimates (LFS), wages of employees by type of work, North American Industry Classification System (NAICS), sex and age group | Variable: 15 years and over, Health care and social assistance, Both sexes, Total employees, Total employees, all wages | Units: Current $CAD Persons x 1,000, 1997-2013. 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; Socioeconomic status; Summary statistics; Official statistics; Immigration; Wage; Personal income","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":[0.002090339,0.002433657,0.003028942,0.007392352,0.00293049,0.004355485,0.005288315,0.001438296,0.08517489],"category_scores_gemma":[0.01595055,0.001798229,0.002131523,0.03817708,0.000551754,0.002133906,0.002198804,0.003341622,0.05339715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04537289,"about_ca_system_score_gemma":0.1134351,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9940879,"about_ca_topic_score_gemma":0.992224,"domain_scores_codex":[0.9961023,0.0002636353,0.0004416669,0.0004845158,0.001762827,0.0009450772],"domain_scores_gemma":[0.9698755,0.001033776,0.0009695049,0.0008031976,0.02588007,0.001438019],"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":[0.00003075861,0.000008618154,0.001121123,0.0002489235,0.00002433596,0.000006143313,0.00001977684,0.00009963478,0.000007762351,0.0002681333,0.9964193,0.001745606],"study_design_scores_gemma":[0.0002715199,0.00002040219,0.04103249,0.001203211,0.00009921793,0.00003288305,0.0005900235,0.0005984463,0.0001955272,0.0007466794,0.9551011,0.0001086518],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006417627,0.00005331193,0.0000214812,0.0001099903,0.00002969911,0.0000155187,0.9989434,0.00005015664,0.0007123004],"genre_scores_gemma":[0.0007830516,0.0002637841,0.00029339,0.000158334,0.00001902017,0.0001200192,0.9943948,0.0000836613,0.003884017],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08517489,"threshold_uncertainty_score":0.3292049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03013600463765588,"score_gpt":0.2737220370122643,"score_spread":0.2435860323746084,"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."}}