{"id":"W4293057876","doi":"10.1109/vtc2022-spring54318.2022.9860972","title":"Support Vector-Based Unsupervised Learning Approaches for Radio Frequency Interference Detection","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Support vector machine; Computer science; Artificial intelligence; Novelty detection; Machine learning; Unsupervised learning; Pattern recognition (psychology); One-class classification; Binary classification; Novelty","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001801633,0.001117935,0.001267799,0.001649966,0.0003996617,0.0009121174,0.00175815,0.0008997447,0.0009175739],"category_scores_gemma":[0.005705095,0.0003842701,0.0008168069,0.001505941,0.000638112,0.001362661,0.0008342636,0.001635322,0.000688888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005319838,"about_ca_system_score_gemma":0.0007099308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001532759,"about_ca_topic_score_gemma":0.001506055,"domain_scores_codex":[0.998123,0.0006557368,0.0001576047,0.0003648503,0.0005990427,0.0000996522],"domain_scores_gemma":[0.9961028,0.001898192,0.0004783249,0.0004048534,0.001046929,0.00006889727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001827544,0.0003092415,0.002882194,0.0002358016,0.0002541821,0.00009438612,0.0001257785,0.2507805,0.009406115,0.01075798,0.003283448,0.7216877],"study_design_scores_gemma":[0.000007064891,0.00005151662,0.000535374,0.000009566111,0.00001136543,0.00004253091,0.00001645976,0.9903836,0.002999279,0.005042713,0.0008857844,0.00001473388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006772363,0.0003110968,0.9917788,0.00006890308,0.00002151686,0.00003493117,0.00005447429,0.0005726668,0.0003852358],"genre_scores_gemma":[0.3620187,0.0006584036,0.6331562,0.0001593944,0.0001554922,0.0003472021,0.0008929531,0.0001522336,0.002459403],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001801633,"threshold_uncertainty_score":0.0095281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03059035772502548,"score_gpt":0.2378231518873359,"score_spread":0.2072327941623104,"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."}}