{"id":"W3083438512","doi":"10.12688/f1000research.25484.2","title":"PUblications Metadata Augmentation (PUMA) pipeline","year":2021,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 Health; Economic and Social Research Council; European Commission; Canadian Institutes of Health Research; National Institute for Health and Care Research; UK Research and Innovation; University of Bristol; Department of Health and Social Care; Wellcome Trust; Medical Research Council; Wellcome","keywords":"Metadata; Computer science; Citation; Pipeline (software); World Wide Web; Information retrieval; Bibliometrics; Data science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"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":[0.002693522,0.000327454,0.0003371267,0.0002179337,0.0003754534,0.0006259558,0.001237345,0.0002889892,0.02916606],"category_scores_gemma":[0.0008983376,0.0003569926,0.0001346631,0.0006538742,0.0004108778,0.0009134565,0.005737201,0.001622747,0.002875173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001146475,"about_ca_system_score_gemma":0.000274451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002791758,"about_ca_topic_score_gemma":0.0008043068,"domain_scores_codex":[0.9943081,0.0008016542,0.0005698992,0.001639866,0.001846995,0.0008334477],"domain_scores_gemma":[0.9970565,0.0003067552,0.0001936554,0.001920233,0.00006302709,0.0004597799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001167792,0.003071725,0.1782448,0.0009499149,0.0004862169,0.0002816023,0.004939783,0.01450307,0.05254157,0.001336571,0.3269286,0.4165993],"study_design_scores_gemma":[0.001453454,0.0001105611,0.5624444,0.0002977454,0.0001910698,0.00003229794,0.002206998,0.0316822,0.01438631,0.003815421,0.3815501,0.001829366],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6231863,0.001231972,0.1586953,0.03352793,0.001024096,0.006609534,0.0009148524,0.0006158674,0.1741941],"genre_scores_gemma":[0.9090808,0.002443479,0.01853421,0.001957412,0.0004575446,0.00165833,0.006887565,0.0002086956,0.05877198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4147699,"threshold_uncertainty_score":0.9998882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1056763213032037,"score_gpt":0.3878501054985689,"score_spread":0.2821737841953651,"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."}}