{"id":"W4318953376","doi":"10.1109/access.2023.3234359","title":"IEEE <i>Access</i> <sup>™</sup> Editorial Board","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Educational Robotics and Engineering","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Agência Nacional de Energia Elétrica; Universidade Federal de Itajubá; Fonds de recherche du Québec – Nature et technologies; Technische Universität München; University of Waterloo; Ministerio de Economía y Competitividad; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Chinese Academy of Sciences; National Science Foundation; Royal Society; Royal Society of Canada; National Aeronautics and Space Administration; University of Manitoba; Air Force Research Laboratory; McMaster University; U.S. Department of Energy; Government of Canada","keywords":"Computer 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000258509,0.0001599673,0.0001567861,0.0001814543,0.0001461938,0.001001182,0.002470277,0.00008954319,0.0000146867],"category_scores_gemma":[0.00005377483,0.0001597377,0.00007093144,0.001042378,0.00002116436,0.001599781,0.0003133989,0.0001970468,0.0003720931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004850297,"about_ca_system_score_gemma":0.0001383724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001056871,"about_ca_topic_score_gemma":0.000005791041,"domain_scores_codex":[0.9984257,0.00001965683,0.0002256096,0.0003891269,0.0005278045,0.0004121106],"domain_scores_gemma":[0.9989514,0.0001816898,0.00005180047,0.0005314924,0.0001321142,0.0001515152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002029214,0.00002754921,0.0005421469,0.00002508489,0.00001691866,0.00001045037,0.0002387982,0.325147,0.0002911086,0.002499044,0.6701908,0.001009094],"study_design_scores_gemma":[0.0007551645,0.00006156651,0.005014428,0.00009090202,0.00002269701,0.000009671342,0.0000337065,0.6526983,0.007638674,0.01273064,0.3199563,0.0009879465],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.156591,0.00007935033,0.474901,0.005559458,0.3550623,0.0004063294,0.00001683444,0.001838457,0.005545229],"genre_scores_gemma":[0.9094112,0.00008317646,0.003265799,0.0007814599,0.08536471,0.00008746287,0.00001372694,0.00005006154,0.0009424374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7528201,"threshold_uncertainty_score":0.9654418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0343959751666223,"score_gpt":0.3088538497330225,"score_spread":0.2744578745664002,"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."}}