{"id":"W6957749107","doi":"10.6068/dp14ba8d4293393","title":"Trend 1987 - 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 (including maternity leave), Total, all industries, Males | Units: # Days, 1987-2013. 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001593676,0.001537387,0.001922075,0.0001949676,0.0004317794,0.001175749,0.001445806,0.001019131,0.001116126],"category_scores_gemma":[0.000329936,0.001540773,7.029632e-7,0.0008241488,0.001308999,0.0006882602,0.001070739,0.001732628,0.00002541778],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007957196,"about_ca_system_score_gemma":0.007342696,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9991864,"about_ca_topic_score_gemma":0.9984852,"domain_scores_codex":[0.9920946,0.001018749,0.001185986,0.002462369,0.0017346,0.001503722],"domain_scores_gemma":[0.9931582,0.002098992,0.001500972,0.001808201,0.0002111387,0.001222481],"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":[0.000443714,0.0001177794,0.02883952,0.001135393,0.0008202025,0.0001924396,0.0000269284,0.00000788157,0.00002233439,0.00005966584,0.9682899,0.00004425671],"study_design_scores_gemma":[0.001295639,0.000169923,0.007872011,0.000177138,0.0006504548,0.0002644557,0.0009491543,0.003816073,3.169481e-8,9.300908e-8,0.9831265,0.001678509],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00400983,0.002527013,0.000005534336,0.000006966356,0.0003255263,0.00149938,0.9913831,0.0001541612,0.00008845708],"genre_scores_gemma":[0.001925366,0.000455749,0.0002282908,0.0001954582,0.000267651,0.00007352579,0.9919366,0.0006473958,0.004270028],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02096752,"threshold_uncertainty_score":0.9998611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0369578719183635,"score_gpt":0.2590557058670512,"score_spread":0.2220978339486877,"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."}}