{"id":"W1559055431","doi":"10.17848/9780880995511","title":"Employee Benefits and Labor Markets in Canada and the United States","year":2000,"lang":"en","type":"preprint","venue":"","topic":"Economics of Agriculture and Food Markets","field":"Economics, Econometrics and Finance","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Point (geometry); Business; Employee benefits; Labour economics; Set (abstract data type); Economics; Finance; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005013634,0.0002855886,0.0006460347,0.0001997007,0.00006364279,0.0001603718,0.0002690264,0.000172612,0.0003175966],"category_scores_gemma":[0.00003852658,0.0002231005,0.000046614,0.000133835,0.0000816008,0.00007620379,0.000356096,0.0004286853,0.000009748059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001709116,"about_ca_system_score_gemma":0.0001130622,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9295195,"about_ca_topic_score_gemma":0.9485608,"domain_scores_codex":[0.9984161,0.00003089563,0.0006594809,0.0005709299,0.00002495337,0.0002976772],"domain_scores_gemma":[0.9990541,0.0002507337,0.0002357682,0.0003357275,0.00002152352,0.0001021663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002792609,0.00002562789,0.8962483,0.0001374136,0.0002499947,0.00001118872,0.0006691981,0.001451661,5.062948e-8,0.08663822,0.01122775,0.00306136],"study_design_scores_gemma":[0.001432376,0.00001268998,0.9188195,0.00005708739,0.00001199215,0.000005715886,0.0001642125,0.001695191,0.000001759338,0.05642816,0.0208768,0.0004944979],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9620489,0.01492515,7.724312e-7,0.003392385,0.0002023934,0.0003692388,0.0007193445,0.000014445,0.01832738],"genre_scores_gemma":[0.9422796,0.05441866,0.00009646302,0.002128462,0.00005531664,0.00005282439,0.000194625,0.00002412924,0.0007499303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03949351,"threshold_uncertainty_score":0.9097773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01201991931097971,"score_gpt":0.1670992978128066,"score_spread":0.1550793785018269,"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."}}