{"id":"W6901663430","doi":"10.6068/dp16249c7840916","title":"Trend 2008 - 2016. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: 282-0201 | Table Name: Labour force survey estimates (LFS), weekly wage distributions of employees by type of work, North American Industry Classification System (NAICS), and sex | Variable: Business, building and other support services-Males -Part-time employees-# Persons-$500 to $799 | Units: # Persons, 2008-2016. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-145.","year":2018,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Earnings; Wage; Census; Summary statistics; Wages and salaries; Economic statistics; Socioeconomic status; Table (database); Descriptive statistics","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":[0.001821264,0.002645685,0.003067991,0.00733899,0.002917238,0.004760673,0.00540959,0.001644995,0.1106112],"category_scores_gemma":[0.01581953,0.00187099,0.002106651,0.03699766,0.0005771695,0.002451212,0.002391515,0.003294888,0.07784897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03870234,"about_ca_system_score_gemma":0.1025734,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9922379,"about_ca_topic_score_gemma":0.9906225,"domain_scores_codex":[0.9965364,0.0002186125,0.0003605027,0.000483896,0.001490987,0.0009096182],"domain_scores_gemma":[0.972219,0.001062957,0.0009350741,0.0008972473,0.02335934,0.001526319],"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":[0.0000224767,0.000006203593,0.0007573711,0.0001741801,0.00001511107,0.000004630449,0.00001479218,0.00007625418,0.000006365576,0.0002005076,0.9975078,0.001214349],"study_design_scores_gemma":[0.0002435617,0.00001610154,0.0277036,0.0009410714,0.00006673539,0.00002473051,0.000466165,0.0004844761,0.000179916,0.0006315657,0.9691506,0.00009151847],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004730441,0.00003757485,0.00001618985,0.0000967868,0.00002808784,0.00001043901,0.9990927,0.00005296135,0.0006179551],"genre_scores_gemma":[0.0005525504,0.0001725782,0.0001940828,0.0001206737,0.00001800343,0.00008801855,0.9952679,0.000088222,0.003497856],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1106112,"threshold_uncertainty_score":0.3700314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02190813212822705,"score_gpt":0.2494055752795344,"score_spread":0.2274974431513073,"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."}}