{"id":"W4398918925","doi":"10.7910/dvn/sczu92/8pchrv","title":"matleave_out_27Oct2015.tab","year":2018,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Healthcare Systems and Public Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Maternity leave; Computer science; Business; Economics; Labour economics; Sick leave","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001530186,0.002598706,0.0017048,0.00495533,0.0009760449,0.004158301,0.003771787,0.002758188,0.2257632],"category_scores_gemma":[0.009348614,0.001009282,0.001840293,0.007196107,0.0006533608,0.002324752,0.002853825,0.001856536,0.2308444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001965049,"about_ca_system_score_gemma":0.002635428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02055358,"about_ca_topic_score_gemma":0.03121547,"domain_scores_codex":[0.9989016,0.0002201165,0.0001289805,0.0003173503,0.0002262789,0.0002057583],"domain_scores_gemma":[0.996983,0.0009493103,0.0002917391,0.0007815206,0.0005848985,0.0004094871],"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":[0.00004024216,0.00001229081,0.0004534412,0.0003994251,0.00002418898,0.000009267364,0.00001392052,0.0001300084,0.00003678073,0.0003511946,0.9973948,0.001134482],"study_design_scores_gemma":[0.0003516769,0.00002194826,0.00262512,0.000453926,0.00003504477,0.00005287425,0.00006424847,0.0005276036,0.0003333089,0.001590773,0.9939106,0.00003295396],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006707085,0.00006651216,0.00003663527,0.0001011649,0.00002948725,0.000006158903,0.9985175,0.0004714761,0.0007041139],"genre_scores_gemma":[0.0003989371,0.0000768649,0.0001439883,0.00008278794,0.00001684607,0.00004542873,0.9982612,0.0001496409,0.0008243566],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7742368,"threshold_uncertainty_score":0.7552532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03500155090121086,"score_gpt":0.3290035271874862,"score_spread":0.2940019762862753,"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."}}