{"id":"W6976723847","doi":"10.6068/dp14ba8f35e2f91","title":"Trend 1989 - 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: 45 years and over, Total, days lost (including maternity leave), Professional, scientific and technical services, Females | Units: # Days, 1989-2013. 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; Work (physics); 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":[0.002240175,0.002363937,0.002894021,0.007908556,0.002974731,0.004234729,0.005453279,0.001467524,0.07991055],"category_scores_gemma":[0.01815475,0.001731986,0.002418142,0.0375019,0.0005408922,0.002108594,0.002194935,0.003282381,0.04549529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04806016,"about_ca_system_score_gemma":0.1158381,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9954804,"about_ca_topic_score_gemma":0.9941826,"domain_scores_codex":[0.9959539,0.0002818404,0.0004912055,0.000494119,0.001807187,0.0009717832],"domain_scores_gemma":[0.9679006,0.001113078,0.00106357,0.0008243112,0.02765308,0.001445314],"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":[0.00003272865,0.000009622774,0.001567328,0.0003096085,0.00003015872,0.000007276137,0.00002712922,0.0001253308,0.000008951638,0.0002802396,0.9958339,0.001767688],"study_design_scores_gemma":[0.0003013987,0.0000242496,0.05720731,0.001442047,0.0001291879,0.00003900048,0.0007234899,0.0007093911,0.0002161679,0.0007300302,0.9383538,0.0001238191],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006895023,0.00005222506,0.00002208204,0.0001008811,0.00002623255,0.00001550003,0.9991032,0.00004630287,0.0005647789],"genre_scores_gemma":[0.0009491459,0.0002753262,0.0003183036,0.0001591009,0.00002065354,0.0001342976,0.9944087,0.00008372561,0.003650724],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07991055,"threshold_uncertainty_score":0.3487025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03207220466657602,"score_gpt":0.2702970424007027,"score_spread":0.2382248377341267,"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."}}