{"id":"W2022799672","doi":"10.1145/2345396.2345450","title":"Higher layer issues in cognitive radio network","year":2012,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cistel Technology (Canada); Concordia University","funders":"","keywords":"Cognitive radio; Computer science; Computer network; Physical layer; Spectrum management; Robustness (evolution); Cognitive network; Software deployment; Software-defined radio; Network layer; Application layer; Layer (electronics); Wireless; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003518705,0.0001404212,0.0001857951,0.00006555007,0.00007424029,0.0000967242,0.0001982236,0.00005967449,0.0002250041],"category_scores_gemma":[0.00001470157,0.0001218243,0.0000504634,0.0005258686,0.00003158454,0.0006250767,0.0001274232,0.0001605592,0.0001327835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003836293,"about_ca_system_score_gemma":0.00001439076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007215855,"about_ca_topic_score_gemma":0.00005700112,"domain_scores_codex":[0.9986678,0.00009238652,0.0001705047,0.0002505489,0.0001635066,0.0006552136],"domain_scores_gemma":[0.999409,0.0002062682,0.00003673491,0.0001852725,0.00003920176,0.0001235311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00004086929,0.0003714758,0.1950787,0.000009256201,0.00009840627,0.0001326906,0.00278786,0.0003013795,0.000151361,0.5163448,0.0464595,0.2382237],"study_design_scores_gemma":[0.00224994,0.000173123,0.8034105,0.0003095468,0.0000319665,0.0001168968,0.0001840319,0.05793577,0.001873577,0.01497865,0.1172329,0.001503166],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1430203,0.01247998,0.5040062,0.005292042,0.00407687,0.0005771546,0.000001279355,0.0006757605,0.3298704],"genre_scores_gemma":[0.9862232,0.00007102492,0.008972231,0.001108948,0.001262045,0.000004171904,0.000001326851,0.00001019196,0.002346881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8432028,"threshold_uncertainty_score":0.4967851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02968524392607262,"score_gpt":0.2810507288995419,"score_spread":0.2513654849734692,"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."}}