{"id":"W6976670208","doi":"10.6068/dp14baa36137985","title":"Trend 1997 - 2011. Statistics Canada. CANSIM: Government - Employment and Remuneration | Country: Canada | Province: Saskatchewan | Table: Labour statistics by business sector industry and non-commercial activity, consistent with the System of National Accounts, by North American Industry Classification System (NAICS) | Variable: Annual average number of hours worked for all jobs, Accommodation and food services, Business sector | Units: , 1997-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-104.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Official statistics; Government (linguistics); Census; Remuneration; Social statistics; Public sector; Summary statistics; Private sector","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":[0.002123034,0.002360564,0.002757889,0.00834462,0.00333526,0.004644014,0.005089252,0.001458057,0.08705141],"category_scores_gemma":[0.01805654,0.001790407,0.001980233,0.04262707,0.0006092358,0.002535887,0.002289048,0.003167578,0.05566712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05478521,"about_ca_system_score_gemma":0.1452029,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.994898,"about_ca_topic_score_gemma":0.9935147,"domain_scores_codex":[0.9956495,0.0003046486,0.0004791018,0.000565835,0.002000067,0.001000848],"domain_scores_gemma":[0.9618528,0.001229073,0.001044735,0.001049022,0.03323511,0.001589288],"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":[0.00002412778,0.000006599083,0.001017301,0.0002289827,0.00002177478,0.000006852413,0.00002204728,0.0001011717,0.000009174964,0.0003450438,0.9966311,0.001585685],"study_design_scores_gemma":[0.0001604883,0.00001334885,0.02793863,0.0008754241,0.00007533263,0.00002825653,0.0005914379,0.0004866905,0.0001924013,0.0006713889,0.9688728,0.00009374922],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006219095,0.00004724484,0.00002438075,0.000127595,0.00002992344,0.00001583249,0.9987589,0.00005362003,0.0008802574],"genre_scores_gemma":[0.0008522315,0.0002823185,0.0003756395,0.000171603,0.00001861008,0.0001258399,0.9934928,0.0001093235,0.004571611],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08705141,"threshold_uncertainty_score":0.3974963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01863776239521182,"score_gpt":0.236171806644187,"score_spread":0.2175340442489752,"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."}}