{"id":"W4398520657","doi":"10.7910/dvn/db11yd","title":"PROSPERED Dataset: Paternity Leave","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001862186,0.0005925929,0.0007104419,0.0003570885,0.0006953719,0.0005345015,0.002708973,0.0005697129,0.03545945],"category_scores_gemma":[0.0003199155,0.0006135707,0.0002518195,0.0004585073,0.0006820412,0.0008391233,0.001122659,0.0007929303,0.2826256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002642442,"about_ca_system_score_gemma":0.0003538312,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02591895,"about_ca_topic_score_gemma":0.01667279,"domain_scores_codex":[0.9948738,0.0006130532,0.0006290508,0.00120685,0.001641351,0.001035866],"domain_scores_gemma":[0.9955838,0.0001040402,0.0005387122,0.003311252,0.0001501883,0.0003120459],"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":[0.00004337232,0.0002076968,0.000902499,0.0002542142,0.0001948461,0.0001491314,0.0001913768,0.000002712014,8.331338e-7,0.0003423432,0.9974127,0.0002982223],"study_design_scores_gemma":[0.0005110853,0.00004178294,0.001099098,0.00008748579,0.0002783455,0.000001374486,0.0004711081,0.00000379227,0.000002107123,0.0000760262,0.9966858,0.0007420007],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009594508,0.000004182849,0.00001358467,0.00002284736,0.003108704,0.001743751,0.9922321,0.0001123091,0.002666565],"genre_scores_gemma":[0.0001805887,0.001829602,0.00009486834,0.0008341828,0.0008768878,0.0000885096,0.9945728,0.00004283775,0.001479751],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2471661,"threshold_uncertainty_score":0.9996316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03458317578973762,"score_gpt":0.3077230664053286,"score_spread":0.273139890615591,"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."}}