{"id":"W6976516471","doi":"10.6068/dp14ba8f1670679","title":"Trend 2005 - 2013. Statistics Canada. CANSIM: Labor - Labor Mobility, Turnover and Work Absences | Country: Canada | Table: Labour force survey estimates (LFS), average days lost for personal reasons per full-time employee by North American Industry Classification System (NAICS), sex and age group | Variable: 25 to 44 years, Other personal (including maternity leave), Forestry, fishing, mining, quarrying, oil and gas, Females | Units: # Days, 2005-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-141.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Economic statistics; Official statistics; Socioeconomic status; Summary statistics; Turnover; Work (physics); Personal income; Population statistics; Social 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":[0.002137698,0.002497028,0.00284493,0.007678399,0.002944723,0.004216575,0.005555457,0.001478246,0.07758882],"category_scores_gemma":[0.01724332,0.001738443,0.002469904,0.03719464,0.000554973,0.002217705,0.002203017,0.003232283,0.04426407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04719022,"about_ca_system_score_gemma":0.1115264,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9957463,"about_ca_topic_score_gemma":0.9945855,"domain_scores_codex":[0.9963375,0.0002567832,0.0004511855,0.000451116,0.00159383,0.0009095772],"domain_scores_gemma":[0.9713047,0.001082239,0.0009986823,0.0007711712,0.02439646,0.001446744],"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":[0.00003320042,0.00000885992,0.001675811,0.0003160497,0.00003061589,0.000007654148,0.00002871397,0.0001262486,0.00000886916,0.0002823972,0.9956506,0.001831086],"study_design_scores_gemma":[0.0002901298,0.00002431092,0.05643905,0.00154691,0.0001344512,0.00004165371,0.0008297254,0.0007848003,0.0002259537,0.0008029426,0.9387481,0.000132014],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007238543,0.00005881605,0.00002337139,0.0001087673,0.00002765218,0.00001411526,0.9990972,0.00004731731,0.0005503551],"genre_scores_gemma":[0.001003187,0.000293093,0.0003114703,0.0001603175,0.00001986442,0.0001221397,0.9945483,0.00007879642,0.003462887],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07758882,"threshold_uncertainty_score":0.3423906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03673243718509202,"score_gpt":0.2630705099820951,"score_spread":0.2263380727970031,"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."}}