{"id":"W4398925518","doi":"10.7910/dvn/sczu92/f4lrmh","title":"maternitylv_3Oct2019.tab","year":2019,"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; Political science; Law; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009765775,0.0004634513,0.001112264,0.0003591885,0.00009784469,0.00008461413,0.0004426329,0.0008327541,0.047203],"category_scores_gemma":[0.0003346065,0.0003964262,0.0001930321,0.0002121514,0.00005785548,0.0001866822,0.0002829587,0.001117728,0.5523793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003159446,"about_ca_system_score_gemma":0.002696748,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01032341,"about_ca_topic_score_gemma":0.0002862504,"domain_scores_codex":[0.9967045,0.0002260561,0.0007839915,0.00073792,0.0007673273,0.0007802339],"domain_scores_gemma":[0.9956073,0.00008822633,0.0003316883,0.002957941,0.0002079806,0.0008069351],"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":[0.00007894546,0.0001097657,0.0001154822,0.004676838,0.00009335244,0.0002994688,0.0000171853,1.019331e-7,0.000003272796,0.00001563476,0.9939953,0.0005946169],"study_design_scores_gemma":[0.0009852912,0.0002453474,0.0003957113,0.0007145688,0.0001268756,0.0003450929,0.00004886443,0.000006101081,0.00000203551,0.000002414133,0.9967893,0.0003383617],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001067931,0.000004810098,0.00000842912,0.0001536079,0.003041971,0.001298218,0.9941044,0.00008591034,0.001195855],"genre_scores_gemma":[0.00003411274,0.0006519991,0.00009374871,0.00416406,0.002010599,0.0000384284,0.987403,0.00005658168,0.00554749],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5051763,"threshold_uncertainty_score":0.9998488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04427533186553979,"score_gpt":0.3310789718447595,"score_spread":0.2868036399792198,"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."}}