{"id":"W6939015601","doi":"10.6068/dp14ba8e06ea885","title":"Trend 1997 - 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 years and over, Other personal (excluding maternity leave), Services-producing sector, Both sexes | Units: # Days, 1997-2013. 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; Official statistics; Economic statistics; Summary statistics; Socioeconomic status; Work (physics); Turnover; Population statistics; Wages and salaries","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":[0.002380408,0.002528741,0.00295957,0.008021615,0.003117746,0.004492458,0.005633782,0.00148051,0.08571121],"category_scores_gemma":[0.01844989,0.001836833,0.002555506,0.03757844,0.0005756834,0.002241172,0.002346776,0.003394473,0.04865458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04874161,"about_ca_system_score_gemma":0.1219441,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9956406,"about_ca_topic_score_gemma":0.9942788,"domain_scores_codex":[0.995846,0.0003013791,0.0004921928,0.0005120226,0.001841666,0.001006859],"domain_scores_gemma":[0.9673951,0.001204641,0.001081216,0.0008975523,0.0278452,0.001576281],"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":[0.00003251884,0.000008935463,0.001427527,0.000302871,0.00003010061,0.000006860811,0.0000266366,0.0001196163,0.000008609638,0.0002803167,0.9959189,0.001837099],"study_design_scores_gemma":[0.0003084148,0.00002401728,0.04915352,0.001422822,0.000126996,0.00003758086,0.0007197855,0.0007106525,0.0002151555,0.0007806672,0.9463736,0.000126854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006711269,0.00005374425,0.00002446017,0.0001083878,0.00002785427,0.0000167322,0.9990232,0.00005397812,0.0006246231],"genre_scores_gemma":[0.0009133443,0.0002792205,0.0003446118,0.0001691744,0.00002062344,0.0001434144,0.9942993,0.00009753391,0.003732833],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08571121,"threshold_uncertainty_score":0.3536468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02627859594368832,"score_gpt":0.2486162970959658,"score_spread":0.2223377011522775,"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."}}