{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006117329,0.001852744,0.001383374,0.01868846,0.001460908,0.005967184,0.002129028,0.001133225,0.06138411],"category_scores_gemma":[0.0279999,0.001330145,0.002208159,0.01190405,0.0005662112,0.005814847,0.006027611,0.002120607,0.05045383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001513291,"about_ca_system_score_gemma":0.004992551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00558506,"about_ca_topic_score_gemma":0.007626246,"domain_scores_codex":[0.9977198,0.0002996951,0.0003631678,0.0006052994,0.0008152688,0.0001967466],"domain_scores_gemma":[0.9872544,0.004870526,0.001355204,0.002808249,0.002925402,0.0007861374],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008524997,0.0002121889,0.01302686,0.003910567,0.0003505538,0.0005141812,0.001484663,0.002135372,0.008521644,0.01412828,0.5558663,0.3989969],"study_design_scores_gemma":[0.0001683132,0.0001291098,0.01012444,0.0003191801,0.0001694498,0.0003460865,0.0004934669,0.01564386,0.01966217,0.02206314,0.9306998,0.0001810435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.007460894,0.001525605,0.1968965,0.001772914,0.0008478871,0.001774431,0.294908,0.4687603,0.02605342],"genre_scores_gemma":[0.0444649,0.001785649,0.4476667,0.0008462197,0.0005456252,0.004921619,0.4319377,0.0367672,0.0310645],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.9938827,"threshold_uncertainty_score":0.2053503,"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."}}