{"id":"W4396882454","doi":"10.48550/arxiv.2405.06563","title":"What Can Natural Language Processing Do for Peer Review?","year":2024,"lang":"en","type":"preprint","venue":"TUbilio (Technical University of Darmstadt)","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Office of Naval Research; Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; Natural Sciences and Engineering Research Council of Canada; European Commission; Australian Government; Alberta Machine Intelligence Institute; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Operationalization; Computer science; Pace; Process (computing); Peer review; Artificial intelligence; Technical peer review; Field (mathematics); Aside; Data science; Political science; Linguistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4265671,0.002524646,0.006876353,0.0189651,0.01910141,0.05686799,0.01319392,0.02320452,0.02692041],"category_scores_gemma":[0.7068497,0.002443429,0.004314145,0.01608667,0.02579925,0.09826195,0.01771663,0.02033819,0.04423434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01395633,"about_ca_system_score_gemma":0.09389085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00779405,"about_ca_topic_score_gemma":0.009958006,"domain_scores_codex":[0.4966576,0.3280882,0.04271554,0.02397704,0.09984814,0.008713415],"domain_scores_gemma":[0.1543416,0.4150675,0.03695793,0.09457058,0.2696136,0.02944885],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001304916,0.0001115447,0.001133075,0.004149311,0.0001666653,0.00034814,0.004063141,0.0004222313,0.0004817678,0.03692666,0.7023188,0.2497481],"study_design_scores_gemma":[0.000115699,0.00005866519,0.001057848,0.003787339,0.00006314616,0.0001752154,0.003304641,0.000843494,0.0003568897,0.1033229,0.8866853,0.0002288459],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.0008590429,0.01430142,0.03752841,0.8856276,0.0362703,0.001133881,0.0006927924,0.002447308,0.02113921],"genre_scores_gemma":[0.06878344,0.06041276,0.3663771,0.3386644,0.1044646,0.009654086,0.005639565,0.005272349,0.04073162],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5734329,"threshold_uncertainty_score":0.7071449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02300005229061961,"score_gpt":0.2823599422652428,"score_spread":0.2593598899746232,"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."}}