{"id":"W6920166961","doi":"10.6068/dp14ba8e5d4f643","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: 15 years and over, Own illness or disability, Professional, scientific and technical services, Both sexes | 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; Economic statistics; Official statistics; Work (physics); Socioeconomic status; Turnover; Summary statistics; 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":[0.002279806,0.002564016,0.003205884,0.008388354,0.003245286,0.004534777,0.005876203,0.001593639,0.08561373],"category_scores_gemma":[0.01910198,0.001904437,0.00261897,0.03977218,0.0006034094,0.00229995,0.002331487,0.00344465,0.04919657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05169594,"about_ca_system_score_gemma":0.1294989,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9958071,"about_ca_topic_score_gemma":0.9944692,"domain_scores_codex":[0.9956987,0.0003005422,0.0005143548,0.0005120377,0.001937522,0.001036905],"domain_scores_gemma":[0.9657032,0.001242885,0.001136607,0.0008780051,0.02942419,0.001615173],"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":[0.00003171971,0.000009541722,0.001394199,0.0003216781,0.00003012487,0.000006587754,0.000026971,0.0001292051,0.000008068252,0.0002874502,0.9960879,0.001666531],"study_design_scores_gemma":[0.0003375005,0.0000247543,0.05042695,0.001513057,0.0001378788,0.00003844305,0.0007333928,0.0007321538,0.0002175135,0.0008080549,0.9448991,0.0001312369],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006447575,0.00005459653,0.00002296662,0.0001016791,0.00002673576,0.00001687202,0.999068,0.00005109205,0.0005936617],"genre_scores_gemma":[0.0009052522,0.0002844289,0.0003339294,0.0001636971,0.00002151257,0.0001514344,0.9944884,0.00009278618,0.003558503],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08561373,"threshold_uncertainty_score":0.3750821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02114560192995784,"score_gpt":0.2626797427064651,"score_spread":0.2415341407765073,"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."}}