{"id":"W4236540890","doi":"10.12688/f1000research.25484.1","title":"PUblications Metadata Augmentation (PUMA) pipeline","year":2020,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Research Data Management Practices","field":"Computer 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":"Puma; Metadata; Open peer review; Pipeline (software); Plant biology; Open science; Neuroscience; Computational biology; World Wide Web; Biology; Computer science; Physiology; Medicine; Data science; Botany; Operating system; Genetics","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","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["scholarly_communication","open_science"],"category_scores_codex":[0.004023038,0.0003191401,0.0003475101,0.0009902354,0.0003122569,0.02073212,0.0158615,0.0001711115,0.000294515],"category_scores_gemma":[0.004216216,0.0003206378,0.0001339896,0.001884852,0.0001552492,0.03713082,0.03303934,0.001979663,0.001023073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002396872,"about_ca_system_score_gemma":0.001022926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005525093,"about_ca_topic_score_gemma":0.0000650021,"domain_scores_codex":[0.9925864,0.001005949,0.0005943836,0.001874209,0.003131271,0.0008077455],"domain_scores_gemma":[0.9926537,0.000717362,0.0003000407,0.0051089,0.0006919337,0.0005280651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002280307,0.0002114808,0.0001934277,0.0003443452,0.0001920328,0.00005644792,0.0001461025,0.0001527603,0.0005458751,0.614028,0.3625067,0.02160001],"study_design_scores_gemma":[0.0004484006,0.00008685888,0.003068372,0.00005679964,0.0000376939,0.000004656186,0.00008312108,0.1458383,0.0009134951,0.02532544,0.8236222,0.0005146772],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00009315323,0.0003880211,0.8351753,0.1355867,0.0003472877,0.001512606,0.0002608831,0.0005867759,0.02604922],"genre_scores_gemma":[0.09431024,0.01801911,0.6034442,0.004209056,0.002559915,0.00488009,0.01874587,0.0003353956,0.2534961],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5887026,"threshold_uncertainty_score":0.9999245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.414620499255469,"score_gpt":0.4835752063025321,"score_spread":0.06895470704706319,"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."}}