{"id":"W4290996429","doi":"10.1109/icc45855.2022.9839170","title":"Intelligent Spectrum Sensing: An Unsupervised Learning Approach Based on Dimensionality Reduction","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Cognitive radio; Artificial intelligence; Machine learning; Dimensionality reduction; Overhead (engineering); Principal component analysis; Unsupervised learning; Spectrum management; Support vector machine; Supervised learning; Reduction (mathematics); Artificial neural network; Wireless","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","sts"],"consensus_categories":[],"category_scores_codex":[0.0008527383,0.0002438724,0.0002119214,0.0003832756,0.001517608,0.0003208604,0.002242772,0.00005078554,0.0003162479],"category_scores_gemma":[0.00007040287,0.0002742713,0.0001356391,0.0005912171,0.0001310784,0.0003023501,0.0006183602,0.001210846,0.00003158144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005050995,"about_ca_system_score_gemma":0.0002193568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001329898,"about_ca_topic_score_gemma":0.00003571357,"domain_scores_codex":[0.9966521,0.001014913,0.0004216632,0.0006783439,0.0009340166,0.0002989405],"domain_scores_gemma":[0.997334,0.0002699831,0.0002312923,0.00172737,0.0003014216,0.0001359193],"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":[0.0001748191,0.001872013,0.00009408491,0.00000515388,0.0001093488,0.00001885242,0.001444016,0.2277416,0.004516908,0.6985542,0.0008941555,0.06457479],"study_design_scores_gemma":[0.0003112415,0.0003310585,0.0003112931,0.00002757944,0.000009271022,0.00005050012,0.0006807584,0.9855174,0.000458792,0.006917873,0.005090153,0.0002941238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05653741,0.0001137093,0.7282799,0.06063478,0.004040244,0.001055634,0.00008679774,0.0009507799,0.1483007],"genre_scores_gemma":[0.9847986,0.00006972943,0.01334184,0.0008800386,0.0001436896,0.00004224721,0.0003463106,0.00002354427,0.000353992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9282612,"threshold_uncertainty_score":0.999971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1074008078944046,"score_gpt":0.3262060940322523,"score_spread":0.2188052861378477,"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."}}