{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009110065,0.001554582,0.00102626,0.004514925,0.0007095629,0.002919711,0.00176991,0.001733757,0.1762946],"category_scores_gemma":[0.00561073,0.0008406526,0.0008764642,0.007566624,0.0004200007,0.001470623,0.001893002,0.00129376,0.1532207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001662581,"about_ca_system_score_gemma":0.002103811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03777149,"about_ca_topic_score_gemma":0.04665077,"domain_scores_codex":[0.9993025,0.0000971584,0.00008135,0.0001862203,0.0001440522,0.0001887268],"domain_scores_gemma":[0.9980198,0.0006026266,0.0002840481,0.0004242471,0.0003434348,0.0003258289],"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":[0.00003380683,0.000009890165,0.0009796788,0.0002531357,0.00001279116,0.00001341315,0.00002012843,0.000149067,0.00004229729,0.0005163071,0.9964377,0.001531877],"study_design_scores_gemma":[0.0001933045,0.00001842144,0.007891394,0.0002661273,0.00002681318,0.000049096,0.00009260816,0.0003810691,0.0003047358,0.0008835119,0.9898637,0.00002910812],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009486128,0.00002648652,0.00002346206,0.00005146419,0.00001191542,0.000003291251,0.9990335,0.0001958435,0.0005592558],"genre_scores_gemma":[0.000666111,0.00005360604,0.00009575741,0.00005759189,0.00001076428,0.00003764181,0.9979405,0.00008887368,0.001049134],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8237054,"threshold_uncertainty_score":0.589764,"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."}}