{"id":"W6901536745","doi":"10.6068/dp14ba8e3d9ca37","title":"Trend 2008 - 2010. 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: 45 years and over, Other personal (excluding maternity leave), Health care and social assistance, Males | Units: # Days, 2008-2010. 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; Summary statistics; Socioeconomic status; Turnover; Work (physics); Social statistics; Population 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":[0.002324621,0.002655506,0.002931026,0.007893291,0.002951217,0.004363606,0.005765405,0.00153451,0.08131383],"category_scores_gemma":[0.01779548,0.001870259,0.002641084,0.03587677,0.0005597923,0.002214917,0.002336595,0.003412826,0.04722176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04758755,"about_ca_system_score_gemma":0.1186988,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9954163,"about_ca_topic_score_gemma":0.9941665,"domain_scores_codex":[0.9960537,0.0002853541,0.0004724188,0.0004961966,0.001723755,0.0009685344],"domain_scores_gemma":[0.9694258,0.001117554,0.001083228,0.0008509458,0.0260036,0.0015189],"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":[0.00003245431,0.000008717789,0.001403022,0.0002999685,0.00002969222,0.000006574498,0.00002417867,0.0001188464,0.000008708349,0.0002533499,0.996105,0.001709587],"study_design_scores_gemma":[0.0003215214,0.00002570153,0.04976564,0.001449027,0.0001319295,0.00003876384,0.0006672067,0.0007859666,0.0002231758,0.000699439,0.9457632,0.0001284611],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006600512,0.00005243726,0.00002197967,0.0001029363,0.00002739122,0.00001492244,0.9991116,0.00005251433,0.0005500694],"genre_scores_gemma":[0.000835434,0.000252854,0.0003181521,0.0001572587,0.00001985224,0.0001306488,0.9948402,0.00008611232,0.003359429],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08131383,"threshold_uncertainty_score":0.3452734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03086872795058008,"score_gpt":0.2640493932289525,"score_spread":0.2331806652783725,"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."}}