{"id":"W4398738227","doi":"10.7910/dvn/fywx4i","title":"PROSPERED Dataset: Sick Leave","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Digital Economy and Work Transformation","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Sick leave; Computer science; Medicine; Physical therapy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005438664,0.000264688,0.0003376511,0.000123618,0.000318563,0.0004658957,0.0009981181,0.0003597227,0.03586622],"category_scores_gemma":[0.0001367392,0.0002783643,0.0001005749,0.0001756979,0.0002498152,0.001932949,0.000157423,0.0003400587,0.4635025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001488485,"about_ca_system_score_gemma":0.0004147213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001781679,"about_ca_topic_score_gemma":0.002775501,"domain_scores_codex":[0.9982102,0.00009792698,0.0003589234,0.0004362585,0.0004300782,0.0004666214],"domain_scores_gemma":[0.9984864,0.00008435157,0.0002063647,0.0009915377,0.00004890729,0.0001824635],"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":[0.00002466033,0.00006123932,0.000003877643,0.00007991614,0.00004007936,0.00001124923,0.0001985757,0.00000301557,1.402869e-7,0.001122636,0.9973714,0.001083246],"study_design_scores_gemma":[0.0002931735,0.0000277646,0.000004670405,0.00006679174,0.00006331581,0.000001199448,0.0004219719,0.000003622741,0.000002028973,0.00007559885,0.9986833,0.0003565261],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000003573257,0.000001125054,0.00001718317,0.00003461774,0.001167791,0.0006873549,0.979596,0.00004627728,0.01844614],"genre_scores_gemma":[0.00003856353,0.0004626774,0.00003663961,0.000600196,0.0004910326,0.00002596367,0.9965435,0.00001407211,0.001787337],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4276363,"threshold_uncertainty_score":0.9999669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02552133676222459,"score_gpt":0.2737812072731134,"score_spread":0.2482598705108889,"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."}}