{"id":"W4398922237","doi":"10.7910/dvn/sczu92/v1rb6y","title":"maternitylv_20Dec2018.tab","year":2018,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Healthcare innovation and challenges","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Maternity leave; Business; Data science; Computer science; Demographic economics; Economics","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":[],"consensus_categories":[],"category_scores_codex":[0.001365244,0.001486981,0.001233396,0.005008943,0.0007763315,0.003559259,0.002165629,0.001931793,0.1589746],"category_scores_gemma":[0.01285216,0.0008738767,0.001146472,0.01080426,0.0004142437,0.001940235,0.002148651,0.001969391,0.1195613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002520326,"about_ca_system_score_gemma":0.004288479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04263405,"about_ca_topic_score_gemma":0.05472597,"domain_scores_codex":[0.9985978,0.0001821529,0.0002474128,0.0003244219,0.000359532,0.0002887389],"domain_scores_gemma":[0.9951711,0.001713605,0.0008642481,0.0006610946,0.001089362,0.0005004499],"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":[0.00004385802,0.00001028355,0.001024726,0.0003300504,0.00001343602,0.00001178145,0.00001487687,0.000106248,0.00001785629,0.0004767035,0.9962835,0.001666709],"study_design_scores_gemma":[0.0003096296,0.00001415072,0.007222708,0.0007123532,0.00003328006,0.00005408728,0.00009293805,0.0002026401,0.0001713924,0.001020917,0.9901399,0.0000260665],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009440411,0.00007192133,0.00002153261,0.0001146942,0.00002555058,0.0000039587,0.9986671,0.0001056013,0.0008952407],"genre_scores_gemma":[0.0007808861,0.0001840035,0.0001051161,0.0001348588,0.00003086009,0.00006796582,0.996541,0.00008839267,0.002066967],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1589746,"threshold_uncertainty_score":0.5318229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05799790760549088,"score_gpt":0.3568050484649425,"score_spread":0.2988071408594516,"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."}}