{"id":"W2936715908","doi":"10.3389/frma.2019.00002","title":"Editorial: Mining Scientific Papers: NLP-enhanced Bibliometrics","year":2019,"lang":"en","type":"editorial","venue":"Frontiers in Research Metrics and Analytics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Deutscher Akademischer Austauschdienst; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Bibliometrics; Library science; Front (military); Geography; Data science; History; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.007395571,0.0004160543,0.0007923582,0.01398771,0.0003020084,0.0007337843,0.001114313,0.002535151,0.000006954014],"category_scores_gemma":[0.03604998,0.0003832955,0.0001770763,0.01948317,0.001096122,0.00001007465,0.0008876004,0.001903309,0.000009076994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001805128,"about_ca_system_score_gemma":0.001250972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004323528,"about_ca_topic_score_gemma":0.00002514666,"domain_scores_codex":[0.9932722,0.0003719862,0.0006211277,0.001413092,0.003151519,0.001170106],"domain_scores_gemma":[0.9959541,0.00119764,0.0002276111,0.0009073794,0.001363613,0.0003496803],"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":[0.0000702455,0.00008431769,0.0007619973,0.0002013077,0.0001133079,0.000008292918,0.00004107602,0.000009031306,0.0004900218,0.00000313412,0.9671426,0.03107467],"study_design_scores_gemma":[0.0008090625,0.0005772045,0.0000334236,0.0001370283,0.00003813004,4.059899e-7,0.0006127045,0.0004032339,0.0003330052,0.0001095329,0.9965058,0.0004404519],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.001128326,0.01728821,0.001931889,0.00006751625,0.9777,0.0002897386,0.000204834,0.0000206707,0.001368797],"genre_scores_gemma":[0.002056991,0.02537539,0.007599588,0.00001662215,0.9528287,0.00003005552,0.0008840338,0.00008947949,0.01111911],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.03063422,"threshold_uncertainty_score":0.9998619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07409031425835062,"score_gpt":0.4044010460789305,"score_spread":0.3303107318205799,"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."}}