{"id":"W6939027484","doi":"10.6068/dp14ba8e934249","title":"Trend 2007 - 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: 55 years and over, Forestry, fishing, mining, quarrying, oil and gas, Both sexes, Part-time, Average weekly wage rate | Units: Current $CAD, 2007-2013. 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; Wage; Economic statistics; Wages and salaries; Summary statistics; Socioeconomic status; Official statistics; Immigration","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":[0.002013737,0.002422172,0.002938402,0.007845096,0.002991098,0.004563329,0.005213271,0.00147589,0.09110533],"category_scores_gemma":[0.01606043,0.001870219,0.002103206,0.04115276,0.0005652431,0.002292947,0.002153653,0.00318881,0.05663739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05019425,"about_ca_system_score_gemma":0.1204805,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9947647,"about_ca_topic_score_gemma":0.9932546,"domain_scores_codex":[0.9958734,0.0002581719,0.0004638036,0.0004764421,0.001922006,0.001006133],"domain_scores_gemma":[0.965288,0.001106396,0.001031726,0.0007990705,0.03021469,0.001560049],"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":[0.00002884253,0.000008414308,0.001094556,0.0002377302,0.00002072374,0.000006030054,0.000020292,0.00009578969,0.000008064181,0.0002627499,0.9964896,0.001727161],"study_design_scores_gemma":[0.0002372715,0.00001973164,0.04146346,0.001118589,0.00009277782,0.00003122632,0.0006298827,0.000587045,0.0002131704,0.0007160989,0.9547813,0.0001093825],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006937924,0.00005521034,0.00002222973,0.0001200072,0.00003242857,0.00001666754,0.9987411,0.0000552804,0.000887682],"genre_scores_gemma":[0.0008481266,0.0002763,0.0003127497,0.0001713439,0.00002014788,0.0001188607,0.9935219,0.00009475424,0.004635846],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09110533,"threshold_uncertainty_score":0.3641865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02749769660527043,"score_gpt":0.2549120424894313,"score_spread":0.2274143458841609,"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."}}