{"id":"W2750782927","doi":"10.1109/tvt.2017.2750538","title":"Formation of Cognitive Personal Area Networks (CPANs) Using Probabilistic Rendezvous","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rendezvous; Probabilistic logic; Node (physics); Computer science; Skewness; Computer network; Protocol (science); Distributed computing; Topology (electrical circuits); Mathematics; Engineering; Artificial intelligence; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001605748,0.0001930493,0.0002820601,0.0003285836,0.0007258459,0.000103225,0.0004918623,0.0002564775,0.000006758192],"category_scores_gemma":[0.00003453564,0.0001940885,0.0001391222,0.0003266329,0.0003497142,0.0004260579,0.00001013732,0.0004383531,0.000004172711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009520046,"about_ca_system_score_gemma":0.00005082317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002740487,"about_ca_topic_score_gemma":0.00007797612,"domain_scores_codex":[0.9987285,0.00004893742,0.0002815108,0.0003888213,0.0002087754,0.0003435262],"domain_scores_gemma":[0.9987884,0.0001211797,0.0002736531,0.0005548197,0.0002085668,0.00005338155],"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":[0.0001202933,0.0005249339,0.00007487931,0.00005534665,0.000235204,0.0002038973,0.000739969,0.07945073,0.007599374,0.003063082,0.00001636098,0.9079159],"study_design_scores_gemma":[0.0005279808,0.0002035443,0.00007298884,0.0002346537,0.00006444917,0.0002555168,0.00008934928,0.9820154,0.01522891,0.001085126,0.00001821816,0.0002038757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1779556,0.00009397554,0.8206913,0.0003995576,0.0002866283,0.0002486136,0.000006586591,0.0001648572,0.0001528925],"genre_scores_gemma":[0.9936569,0.000049425,0.006183495,0.00003889469,0.00003008753,0.00001568559,0.000001493885,0.00001428201,0.000009783374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.907712,"threshold_uncertainty_score":0.7914697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0247849574802753,"score_gpt":0.2534659347376908,"score_spread":0.2286809772574155,"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."}}