{"id":"W2033611811","doi":"10.3390/s130404884","title":"Estimation of Distribution Algorithm for Resource Allocation in Green Cooperative Cognitive Radio Sensor Networks","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Toronto Metropolitan University","funders":"","keywords":"Relay; Computer science; Throughput; Heuristic; Resource allocation; Mathematical optimization; Cognitive radio; Optimization problem; Computer network; Wireless; Power (physics); Algorithm; Telecommunications","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":[0.001933576,0.0009737891,0.000998735,0.0008121012,0.0006176441,0.0009010736,0.001268191,0.0008410345,0.001167581],"category_scores_gemma":[0.005049607,0.0004389447,0.0004396677,0.001007318,0.0008712947,0.001188492,0.001179968,0.0009232298,0.0002245508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001686649,"about_ca_system_score_gemma":0.001914711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006687971,"about_ca_topic_score_gemma":0.00613112,"domain_scores_codex":[0.9992024,0.0003494703,0.00002850133,0.0001225371,0.0001887021,0.0001084686],"domain_scores_gemma":[0.9981159,0.001351981,0.0001671502,0.00006767908,0.0002440727,0.00005329008],"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":[0.00004938781,0.00002899622,0.0002696708,0.00002547108,0.00001592001,0.00001873951,0.00004245721,0.9648489,0.0005404474,0.006219684,0.000488098,0.02745226],"study_design_scores_gemma":[0.000005553558,0.000006800139,0.00002107373,0.000001803441,0.000001587302,0.000003722563,0.000004680709,0.9982284,0.0001243963,0.001503565,0.00009675943,0.000001700451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007542785,0.0001664758,0.9910211,0.0001017319,0.00001296445,0.00003358761,0.00001269746,0.0001314205,0.0009773175],"genre_scores_gemma":[0.6926988,0.0003984135,0.3039369,0.0001971049,0.0000395679,0.0003751371,0.0001103546,0.00008416402,0.002159498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006687971,"threshold_uncertainty_score":0.01329809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02187134025918104,"score_gpt":0.2676694551101651,"score_spread":0.245798114850984,"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."}}