{"id":"W4255452789","doi":"10.1109/trpms.2021.3135118","title":"Low-Temperature Plasma for Biology, Hygiene, and Medicine: Perspective and Roadmap","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Radiation and Plasma Medical Sciences","topic":"Plasma Applications and Diagnostics","field":"Medicine","cited_by":153,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Engineering and Physical Sciences Research Council; Vlaamse regering; Polytechnique Montréal; Huazhong University of Science and Technology; Universiteit Antwerpen; North Carolina State University; Centre National de la Recherche Scientifique; College of Medicine, Drexel University; Fonds Wetenschappelijk Onderzoek; Queensland University of Technology; Old Dominion University; Drexel University; George Washington University","keywords":"Engineering ethics; Plasma medicine; Field (mathematics); Perspective (graphical); Nanotechnology; Computer science; Atmospheric-pressure plasma; Engineering; Physics; Plasma; Artificial intelligence; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003446885,0.0001378111,0.0002375963,0.0001416557,0.0004885453,0.00004126058,0.00005229972,0.0002035203,0.0001427291],"category_scores_gemma":[0.00028716,0.000102279,0.00003182406,0.0003575395,0.0008460934,0.00008878378,0.000002115537,0.00021423,0.000003368957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002940017,"about_ca_system_score_gemma":0.0002675293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004768441,"about_ca_topic_score_gemma":0.0001487537,"domain_scores_codex":[0.99886,0.00003704831,0.0002043888,0.0004372673,0.0002633482,0.0001979475],"domain_scores_gemma":[0.9983211,0.001038122,0.00004624527,0.00009412799,0.0001086756,0.0003917147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006786031,0.001730805,0.003883084,0.0005124585,0.0004432428,0.00009771866,0.006040788,0.0006039386,0.03719292,0.1440325,0.0092732,0.7955108],"study_design_scores_gemma":[0.04336387,0.01210702,0.03475961,0.00228437,0.001743899,0.004565865,0.03058975,0.3450393,0.3955253,0.02019292,0.1071011,0.002726902],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9538131,0.0007188755,0.007657148,0.03599269,0.0003816653,0.0003664572,0.00008054491,0.00004604786,0.000943431],"genre_scores_gemma":[0.9932467,0.003829123,0.001257984,0.001228714,0.0001211216,0.00006365635,0.00001572976,0.000006668817,0.0002303372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7927839,"threshold_uncertainty_score":0.4170818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02074411593128633,"score_gpt":0.3182356570973907,"score_spread":0.2974915411661043,"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."}}