{"id":"W4408358675","doi":"10.1109/vcc63113.2024.10914398","title":"Advancing Spectrum Management with Machine Learning: A System Identification Case Study","year":2024,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Identification (biology); Computer science; Artificial intelligence; Machine learning","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":[0.003639848,0.0006342573,0.0004737034,0.0009543838,0.001077565,0.00130276,0.00137875,0.002031882,0.001057738],"category_scores_gemma":[0.007633701,0.0002596388,0.0004759734,0.0007982825,0.001356663,0.00190267,0.001292301,0.001283484,0.0002490093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001375854,"about_ca_system_score_gemma":0.001167898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007376271,"about_ca_topic_score_gemma":0.006706633,"domain_scores_codex":[0.9975179,0.001330017,0.0001133905,0.00026509,0.0005782158,0.0001953149],"domain_scores_gemma":[0.992235,0.005571438,0.0004064647,0.0007875591,0.0008192271,0.0001802681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008357615,0.001743831,0.03123816,0.0005569209,0.0001533974,0.004855633,0.002389142,0.7946473,0.01354297,0.02450767,0.003204468,0.1223247],"study_design_scores_gemma":[0.00006833743,0.0003426972,0.002604676,0.00004009829,0.00002682725,0.0005298916,0.0006303139,0.9747526,0.01295073,0.005077078,0.002946525,0.00003017374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7079055,0.0004487477,0.2776433,0.001761712,0.00006164393,0.0008366493,0.0002784033,0.0008515568,0.01021255],"genre_scores_gemma":[0.9320926,0.0001226524,0.06600638,0.0001004614,0.00001917834,0.0001158363,0.00007923582,0.00002483543,0.001438707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007376271,"threshold_uncertainty_score":0.01924956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008513144749548915,"score_gpt":0.2635133347885008,"score_spread":0.2550001900389519,"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."}}