{"id":"W4398939653","doi":"10.7910/dvn/sczu92/nak4at","title":"maternitylv_5Feb2019CSVversion.tab","year":2019,"lang":"it","type":"dataset","venue":"Harvard Dataverse","topic":"Social and Demographic Issues in Germany","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Maternity leave; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity","insufficient_payload"],"category_scores_codex":[0.001634411,0.001274606,0.001784524,0.0007142674,0.001851645,0.0001533055,0.002699433,0.003088445,0.3682518],"category_scores_gemma":[0.0006520047,0.00124813,0.0006436733,0.0007884441,0.0005206213,0.0006910436,0.003623443,0.004136604,0.9615625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004371828,"about_ca_system_score_gemma":0.0008790838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002080176,"about_ca_topic_score_gemma":0.00008247787,"domain_scores_codex":[0.9912112,0.001674251,0.00172481,0.001726039,0.001511364,0.002152281],"domain_scores_gemma":[0.9916227,0.001027233,0.001343171,0.004822888,0.0004329033,0.0007510784],"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":[0.0003464099,0.0003346196,0.001252807,0.002491708,0.0003572506,0.000271499,0.0009621039,0.000001661234,0.00001597485,0.0002172202,0.9935565,0.0001922411],"study_design_scores_gemma":[0.002232221,0.0001802922,0.0009087018,0.001534383,0.0007401874,0.00001515458,0.002686881,0.00003207628,0.000006127263,0.0001472871,0.9901956,0.001321112],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009559248,0.00001038075,0.00002555085,0.00006801336,0.0248159,0.002389373,0.9647213,0.0002091247,0.00680444],"genre_scores_gemma":[0.0002598108,0.004678635,0.0002101402,0.004608665,0.004944374,0.0000924971,0.9469285,0.0001566243,0.03812078],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5933107,"threshold_uncertainty_score":0.9995397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04624650788836197,"score_gpt":0.3522881915172748,"score_spread":0.3060416836289128,"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."}}