{"id":"W4225395043","doi":"10.1109/radarconf2248738.2022.9764256","title":"[Front matter]","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Radar Conference (RadarConf22)","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"Air Force Research Laboratory; Air Force Institute of Technology; Università di Pisa; Army Research Laboratory; University of Electronic Science and Technology of China; Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek; Politechnika Warszawska; University of Toronto; Università degli Studi di Napoli Federico II; University of Oklahoma; Arizona State University; IEEE Foundation; Aalto-Yliopisto; Research Institute, Georgia Institute of Technology","keywords":"Front (military); Creativity; Systems engineering; Engineering; Radar; Engineering management; Computer science; Phased array; Telecommunications; Political science; Mechanical engineering","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000186819,0.0002275374,0.000231207,0.0001479262,0.0001881767,0.00004788037,0.0004300184,0.00006293049,0.02354333],"category_scores_gemma":[0.000007355351,0.0002507321,0.00007759819,0.0002416311,0.00005623238,0.0001107705,0.0000769637,0.0005386054,0.0009591325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002123362,"about_ca_system_score_gemma":0.00008853971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007551241,"about_ca_topic_score_gemma":0.00001069106,"domain_scores_codex":[0.998535,0.00004277401,0.0002714994,0.0002830776,0.0004316886,0.000435898],"domain_scores_gemma":[0.9993491,0.00003370553,0.00003185403,0.0003783153,0.00002398367,0.0001831134],"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":[0.00003084487,0.0002220726,0.0006044267,0.0006345291,0.0002210528,0.00007765405,0.002925446,0.004880979,0.06773558,0.002634784,0.8630596,0.05697301],"study_design_scores_gemma":[0.0005240739,0.0001132441,0.00302627,0.00003409161,0.00003822914,0.0001126008,0.0008540868,0.0316239,0.004643774,0.00102722,0.9571477,0.000854854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6749188,0.005734348,0.09742725,0.009408468,0.04131411,0.001707199,0.0009441275,0.004775865,0.1637698],"genre_scores_gemma":[0.9927881,0.00008236239,0.00105642,0.0003028008,0.0002812431,0.0001014185,0.00008840558,0.00006236789,0.005236887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3178693,"threshold_uncertainty_score":0.9999945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01121144473774347,"score_gpt":0.1984968230855572,"score_spread":0.1872853783478138,"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."}}