{"id":"W6920741584","doi":"10.6084/m9.figshare.12228167","title":"Additional file 1 of The PRECISE (PREgnancy Care Integrating translational Science, Everywhere) database: open-access data collection in maternal and newborn health","year":2020,"lang":"en","type":"article","venue":"Open MIND","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Data collection; Health care; Health data; MEDLINE; Patient data","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":["open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002632321,0.001004912,0.001086598,0.00374044,0.0006845167,0.001918662,0.001737435,0.00116411,0.7065023],"category_scores_gemma":[0.03482193,0.000572558,0.000803722,0.006588498,0.0003799371,0.001822611,0.001746819,0.001133461,0.1182009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001282413,"about_ca_system_score_gemma":0.002643254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00824011,"about_ca_topic_score_gemma":0.01376383,"domain_scores_codex":[0.9986958,0.0002719102,0.0003354154,0.0003033091,0.0002512488,0.0001423847],"domain_scores_gemma":[0.9705377,0.02302896,0.001700025,0.00117146,0.002646042,0.00091579],"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":[0.0002498403,0.00005742881,0.002606515,0.003253548,0.00005043851,0.00004813822,0.00008334046,0.0002467634,0.0001141111,0.0009353354,0.9843488,0.008005748],"study_design_scores_gemma":[0.001932129,0.0001141139,0.02013181,0.00303528,0.0002058321,0.0003666244,0.0005635694,0.0008188947,0.0009502779,0.01036009,0.9613929,0.0001284438],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007598581,0.0000158923,0.0001756259,0.00005948046,0.00001066265,0.000039857,0.9990187,0.0001474951,0.0004563509],"genre_scores_gemma":[0.003677363,0.0001618331,0.003441037,0.0003969604,0.00006403543,0.001214564,0.9875088,0.0005817476,0.002953617],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9982626,"threshold_uncertainty_score":0.4186389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1040726669626449,"score_gpt":0.3885758324642908,"score_spread":0.2845031655016459,"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."}}