{"id":"W2159875031","doi":"10.1109/acssc.2009.5470166","title":"Location-aware cognitive sensing for maximizing network capacity","year":2009,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Cognitive radio; Computer science; Cognitive network; Transmitter; Bayesian probability; Cognition; Mathematical optimization; Computer network; Artificial intelligence; Telecommunications; Mathematics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002879295,0.0001673741,0.0001984951,0.0000567895,0.0003949307,0.0002375942,0.0001758782,0.00006278392,0.00000391458],"category_scores_gemma":[0.00007115968,0.0001624243,0.00008689464,0.0005205312,0.00003254376,0.0003498839,0.00003973194,0.0001242455,0.00001031331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005235546,"about_ca_system_score_gemma":0.00005100358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001816919,"about_ca_topic_score_gemma":0.00004702786,"domain_scores_codex":[0.9986578,0.00004892272,0.0002133806,0.0004290596,0.0001567065,0.0004941681],"domain_scores_gemma":[0.9988632,0.0003774953,0.00008149273,0.0002173263,0.0003584355,0.0001020835],"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":[0.00002426911,0.0000394337,0.00008688421,0.00001162296,0.00003545081,0.00001650695,0.0004487957,0.001876466,0.0001191859,0.06983358,0.001791518,0.9257163],"study_design_scores_gemma":[0.0006088392,0.000162682,0.003079853,0.0002266889,0.00002185119,0.00007343855,0.00008972284,0.9579586,0.001438613,0.03528439,0.0006471985,0.0004080993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007081791,0.0001235006,0.9856225,0.00180204,0.0002943602,0.0003175833,8.941521e-7,0.0002689142,0.00448844],"genre_scores_gemma":[0.8768631,0.000006868329,0.120143,0.002388298,0.0005090368,0.00000128747,0.000004173457,0.000008015543,0.00007621181],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9560822,"threshold_uncertainty_score":0.6623469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02570586292281415,"score_gpt":0.2489651307245033,"score_spread":0.2232592678016892,"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."}}