{"id":"W6976560528","doi":"10.6068/dp14ba8e28cc24","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 to 44 years, Total, days lost (including maternity leave), Health care and social assistance, 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":"History and Theory of Mathematics","field":"Mathematics","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; 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":[0.002541146,0.002563302,0.003087735,0.008573318,0.003111399,0.004418152,0.005714295,0.001509016,0.07977463],"category_scores_gemma":[0.0193136,0.001869291,0.00262714,0.03966237,0.0005921815,0.002242179,0.002360857,0.003514139,0.04523316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05286583,"about_ca_system_score_gemma":0.1319583,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9958604,"about_ca_topic_score_gemma":0.994594,"domain_scores_codex":[0.9955916,0.000321125,0.0005356845,0.0005080722,0.001969734,0.001073821],"domain_scores_gemma":[0.9655554,0.001214992,0.001144647,0.0008907925,0.02956801,0.001626063],"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":[0.00003408668,0.0000101435,0.001599197,0.0003313546,0.00003331914,0.000007250313,0.00002903975,0.000135512,0.000008984288,0.0003034797,0.9955744,0.001933133],"study_design_scores_gemma":[0.000307706,0.0000259966,0.05594372,0.00157169,0.0001392401,0.00003946571,0.000761765,0.0007601148,0.0002195811,0.0008033919,0.9392958,0.0001314746],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007401697,0.00005994866,0.00002595674,0.0001134545,0.00002922484,0.00001829102,0.9990116,0.00005290794,0.0006145464],"genre_scores_gemma":[0.0009883937,0.0003178293,0.0003772979,0.000175546,0.00002247561,0.0001617654,0.9939675,0.00009773663,0.003891467],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07977463,"threshold_uncertainty_score":0.3835703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03781441406661793,"score_gpt":0.2747345289704332,"score_spread":0.2369201149038152,"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."}}