{"id":"W4281676012","doi":"10.1109/isit50566.2022.9834613","title":"MetaSSD: Meta-Learned Self-Supervised Detection","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Information Theory (ISIT)","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Research Foundation of Korea","keywords":"Computer science; Viterbi algorithm; Artificial intelligence; Machine learning; Symbol (formal); Channel (broadcasting); Meta learning (computer science); Detector; Supervised learning; Semi-supervised learning; Artificial neural network; Hidden Markov model; Task (project management)","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001978387,0.0002988701,0.0002863317,0.0006368827,0.0007297296,0.0004786692,0.001787859,0.00008215974,0.001698157],"category_scores_gemma":[0.00008616294,0.0003001232,0.0003311392,0.0008049228,0.00003289535,0.003675403,0.000352684,0.0004733457,0.0007371765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000838569,"about_ca_system_score_gemma":0.00008856061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000196447,"about_ca_topic_score_gemma":0.000002167609,"domain_scores_codex":[0.9959814,0.0006766441,0.000859693,0.0004454008,0.001744004,0.0002928557],"domain_scores_gemma":[0.997758,0.0003790019,0.0006351714,0.0007531408,0.0003533262,0.0001213319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003371636,0.0003885115,0.00004550345,0.00002464457,0.001664437,0.000006705885,0.004139797,0.1592159,0.020865,0.7698121,0.002442128,0.04105815],"study_design_scores_gemma":[0.001398195,0.0003566208,0.0005071945,0.000005804191,0.0002239248,0.00008050656,0.0004496104,0.84952,0.03079149,0.02219572,0.0938009,0.0006700602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02148816,0.00003835329,0.9303285,0.008891102,0.006496328,0.0009709954,0.0001377195,0.001360921,0.03028794],"genre_scores_gemma":[0.9928529,0.00001595067,0.002282375,0.002891945,0.000160721,0.0007546747,0.0001789518,0.00002246901,0.000840006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9713647,"threshold_uncertainty_score":0.9999451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02604886461964958,"score_gpt":0.2520466213999967,"score_spread":0.2259977567803471,"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."}}