{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005174499,0.001968037,0.001320442,0.01515478,0.001493833,0.006236857,0.002405569,0.001259424,0.06844167],"category_scores_gemma":[0.02654345,0.001475838,0.002424782,0.01069291,0.0006509167,0.007078007,0.007056375,0.002297502,0.06376718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00154638,"about_ca_system_score_gemma":0.00455256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005598371,"about_ca_topic_score_gemma":0.007517611,"domain_scores_codex":[0.9976633,0.00029922,0.0003528188,0.0005697547,0.0009053932,0.0002095263],"domain_scores_gemma":[0.9892564,0.003717958,0.001057428,0.002467917,0.002740641,0.0007597017],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008533229,0.0002005764,0.01001601,0.003212184,0.0002775618,0.0005389971,0.001511054,0.00218077,0.007703387,0.01279248,0.6256954,0.3350182],"study_design_scores_gemma":[0.0001725871,0.0001494209,0.006941486,0.0003056101,0.0001368039,0.0003573472,0.0005532897,0.01888882,0.02105558,0.02308726,0.9281582,0.0001935617],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.005712693,0.001120845,0.16583,0.001625909,0.0007548259,0.00137527,0.1995862,0.5993556,0.02463877],"genre_scores_gemma":[0.0467823,0.001632125,0.4376442,0.001268822,0.0005686071,0.00410202,0.4132523,0.05873394,0.0360158],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9948255,"threshold_uncertainty_score":0.2289602,"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."}}