{"id":"W6901511142","doi":"10.6068/dp14ba7d41fd884","title":"Trend 1999 - 2013. Statistics Canada. CANSIM: Labor - Employment and Unemployment | 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 to 24 years, Other personal (excluding maternity leave), Accommodation and food services, Both sexes | Units: # Days, 1999-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-136.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Unemployment; Census; Official statistics; Economic statistics; Summary statistics; Socioeconomic status; Social statistics; Descriptive statistics; General Social Survey; Accommodation","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":[0.002276794,0.002337089,0.002912358,0.007572954,0.003202614,0.00475312,0.005183112,0.001460111,0.09585865],"category_scores_gemma":[0.01867867,0.001876,0.002284097,0.03634545,0.0005611489,0.002399816,0.00233283,0.00307951,0.05471407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05030981,"about_ca_system_score_gemma":0.1273841,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9953135,"about_ca_topic_score_gemma":0.9937754,"domain_scores_codex":[0.995634,0.0003091635,0.0005063831,0.000538451,0.001970025,0.001042014],"domain_scores_gemma":[0.9638545,0.001367787,0.001206402,0.0009462791,0.0308031,0.00182188],"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":[0.00003476637,0.000008944619,0.001205406,0.0002651036,0.00002437213,0.00000649155,0.00002327087,0.0001069781,0.000008845168,0.0002666193,0.9963048,0.001744505],"study_design_scores_gemma":[0.0002723344,0.00002224837,0.04080237,0.001225219,0.00009857853,0.00003288485,0.0006448764,0.0006487967,0.0002239401,0.0006577849,0.9552556,0.0001153757],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006896673,0.00004881052,0.00002404937,0.0001150525,0.00003001811,0.00001659387,0.9987724,0.00006446947,0.0008595852],"genre_scores_gemma":[0.0009416284,0.0002889609,0.0003601687,0.0001823439,0.00002258541,0.0001339976,0.992774,0.0001258486,0.00517048],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09585865,"threshold_uncertainty_score":0.3650249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03078547623874553,"score_gpt":0.2515179978860527,"score_spread":0.2207325216473072,"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."}}