{"id":"W6976246658","doi":"10.6068/dp14ba8b44fd412","title":"Trend 2001 - 2012. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Average weekly earnings (SEPH), by type of employee for selected industries classified using the North American Industry Classification System (NAICS) | Variable: Employees paid by the hour, Arts, entertainment and recreation, Excluding overtime | Units: Current $CAD, 2001-2012. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-145.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Earnings; Overtime; Census; Economic statistics; Wages and salaries; Summary statistics; Official statistics; Socioeconomic status; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005137351,0.0004591918,0.000550684,0.00006044234,0.000389134,0.0004335894,0.001281979,0.0001958865,0.00006394273],"category_scores_gemma":[0.0001125726,0.0003621448,3.16294e-7,0.0007390644,0.0002905852,0.0003041452,0.0004142265,0.0007603195,0.000001167726],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002117073,"about_ca_system_score_gemma":0.005930423,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9821557,"about_ca_topic_score_gemma":0.9073735,"domain_scores_codex":[0.9969383,0.000372767,0.0006513632,0.0008040104,0.0007426566,0.0004908286],"domain_scores_gemma":[0.9959404,0.001115682,0.001128261,0.001273913,0.0002758096,0.0002658936],"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":[0.00002995374,0.00004054447,0.0008736169,0.0001415333,0.0001821446,0.000002010047,0.00001573406,0.00007780303,0.00001098681,0.001187545,0.9969457,0.0004923904],"study_design_scores_gemma":[0.0003020792,0.00006881792,0.0001770259,0.00005192638,0.0001771328,0.00001786016,0.0002255152,0.02381632,1.292371e-7,5.99524e-7,0.974775,0.0003876454],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002951899,0.001448686,0.002940169,0.00003421434,0.0001886184,0.0007610092,0.9944502,0.00005479895,0.00009279886],"genre_scores_gemma":[0.0003354851,0.0003463308,0.0004445678,0.0001686937,0.0001097892,0.0000433483,0.997701,0.00009615474,0.0007545879],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07478219,"threshold_uncertainty_score":0.9998831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05347516366912171,"score_gpt":0.2764403642952088,"score_spread":0.2229652006260871,"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."}}