{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001306283,0.001355207,0.001358927,0.001986291,0.0007526858,0.001676543,0.002619741,0.002064344,0.04190021],"category_scores_gemma":[0.008530051,0.0005161163,0.001221541,0.004035607,0.0002909651,0.0008582543,0.001585645,0.001873503,0.0460557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00131312,"about_ca_system_score_gemma":0.002052043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03622854,"about_ca_topic_score_gemma":0.0623074,"domain_scores_codex":[0.998861,0.0002566832,0.0001875019,0.0003245092,0.0002165341,0.0001538767],"domain_scores_gemma":[0.9975349,0.0006349481,0.0004404458,0.0004120444,0.0007270814,0.0002505746],"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":[0.00009157749,0.00002386337,0.001992224,0.0003379443,0.00003969693,0.00002080602,0.00001888987,0.0001945179,0.00002992889,0.0003980281,0.9948592,0.001993412],"study_design_scores_gemma":[0.001006599,0.00006067874,0.02484285,0.0007890401,0.0001149095,0.000177946,0.0001636732,0.001227026,0.0002366064,0.002700589,0.9686049,0.00007528064],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002829901,0.0000918087,0.00005596584,0.0001223497,0.00002251773,0.00001115412,0.9988769,0.000106551,0.0004297732],"genre_scores_gemma":[0.0008665771,0.00007621988,0.0002269465,0.0001152332,0.00001840132,0.00009573244,0.9978687,0.00003297048,0.0006992402],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04190021,"threshold_uncertainty_score":0.1401702,"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."}}