{"id":"W2276047994","doi":"10.1016/j.phycom.2013.05.001","title":"A history-aware greedy channel restoration scheme for cognitive radio-based LTE networks","year":2013,"lang":"en","type":"article","venue":"Physical Communication","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Polytechnique Montréal","keywords":"Computer science; Cognitive radio; Computer network; Scheme (mathematics); Channel (broadcasting); Telecommunications; Wireless","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009107318,0.0007420565,0.001063516,0.0007730714,0.001353007,0.0008593086,0.002019952,0.0007034182,0.001375654],"category_scores_gemma":[0.002790649,0.0003179629,0.0003823871,0.0006560345,0.0007851584,0.001245421,0.001733172,0.0008091072,0.0002654889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009722326,"about_ca_system_score_gemma":0.001893418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005015745,"about_ca_topic_score_gemma":0.01132209,"domain_scores_codex":[0.9993854,0.0001119581,0.00003454194,0.0001109815,0.0002030247,0.0001541099],"domain_scores_gemma":[0.9988172,0.000447669,0.0001140837,0.0002011292,0.0002824181,0.0001374905],"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":[0.001348607,0.0004371533,0.001598198,0.000201216,0.0001158696,0.0003941816,0.0003490379,0.5270014,0.04156734,0.02647109,0.006842318,0.3936735],"study_design_scores_gemma":[0.00002409186,0.000090727,0.0002143748,0.000006745522,0.00002365428,0.0001239272,0.00004349254,0.9912077,0.002469663,0.005178939,0.0005902852,0.0000263198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04536865,0.0006967268,0.9499721,0.0002190657,0.0001977907,0.00009197088,0.00006676235,0.0005781634,0.0028088],"genre_scores_gemma":[0.9079852,0.0002567708,0.08950323,0.0001297046,0.0001101876,0.00006173923,0.00008692225,0.00003236372,0.001833881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005015745,"threshold_uncertainty_score":0.009973049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03165424981752435,"score_gpt":0.2575854960317833,"score_spread":0.225931246214259,"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."}}