{"id":"W6901816317","doi":"10.6068/dp14ba8cafb6217","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: 15 years and over, Total, days lost (including maternity leave), Agriculture, 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; Summary statistics; Socioeconomic status; Work (physics); Turnover; Population statistics; Social 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":[0.002146664,0.002441081,0.002971873,0.007585795,0.003088347,0.004275995,0.005707975,0.001510406,0.08482193],"category_scores_gemma":[0.01779531,0.001767636,0.002448752,0.03675734,0.0005493312,0.002160557,0.002231519,0.003220653,0.04848454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04755916,"about_ca_system_score_gemma":0.1150927,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9955076,"about_ca_topic_score_gemma":0.9941224,"domain_scores_codex":[0.9960787,0.0002785463,0.0004677878,0.0004928227,0.001732281,0.0009497756],"domain_scores_gemma":[0.9687319,0.001128959,0.001052855,0.0008349468,0.02677381,0.001477604],"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":[0.00003227982,0.000009358919,0.001461726,0.0003032641,0.00002899597,0.000006688415,0.00002544896,0.0001223926,0.000008457296,0.0002698303,0.99607,0.001661606],"study_design_scores_gemma":[0.0003289783,0.0000238883,0.05242263,0.001384908,0.0001291669,0.0000372243,0.0006776816,0.0007069755,0.0002120557,0.0007448674,0.9432092,0.0001224152],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000626723,0.00004929984,0.00002095043,0.00009430551,0.00002473494,0.00001540046,0.999114,0.00004680323,0.0005717794],"genre_scores_gemma":[0.0008930762,0.0002547661,0.0003116603,0.0001526034,0.00001980207,0.0001369697,0.9946938,0.00008487399,0.003452493],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08482193,"threshold_uncertainty_score":0.3450674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03116388071872511,"score_gpt":0.2545645010600036,"score_spread":0.2234006203412784,"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."}}