{"id":"W2076532147","doi":"10.1109/qbsc.2012.6221367","title":"CCAWSN: A cognitive communication architecture for wireless sensor networks","year":2012,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Wireless sensor network; Architecture; Cognitive architecture; Cognitive network; Cognitive radio; Context (archaeology); Focus (optics); Computer network; Wireless; Cognition; Key distribution in wireless sensor networks; Wireless network; Network architecture; Human–computer interaction; Computer architecture; 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":[0.0004252523,0.000213767,0.0002064489,0.00006797862,0.0002695903,0.0001490487,0.0008149898,0.0001534166,0.0000117106],"category_scores_gemma":[0.00004278876,0.0001876318,0.0001149065,0.0003733703,0.00009126292,0.0003297843,0.0002961855,0.0002565248,0.00002089842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000364246,"about_ca_system_score_gemma":0.0000185639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001237399,"about_ca_topic_score_gemma":0.00001839142,"domain_scores_codex":[0.9983596,0.0001724405,0.0002501366,0.0003212001,0.0002066788,0.0006899731],"domain_scores_gemma":[0.9977612,0.000995859,0.0001284949,0.0007487692,0.0001726509,0.0001930167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001040056,0.0007760336,0.003191686,0.00004285847,0.0001636231,0.000003173934,0.003241588,0.3402919,0.0003572178,0.4222933,0.006053742,0.2234808],"study_design_scores_gemma":[0.0007654642,0.00007043972,0.0008455808,0.00007610124,0.00002213314,0.00003119211,0.0001394695,0.9891372,0.001152619,0.0002598974,0.007059842,0.0004400826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02313229,0.0006247287,0.9706061,0.0007188323,0.0003899459,0.000423303,0.000002624537,0.0003869944,0.003715117],"genre_scores_gemma":[0.8836824,0.00004253033,0.1143929,0.0009542962,0.0002751632,0.00008755158,0.00002989905,0.0000251983,0.0005100696],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8605501,"threshold_uncertainty_score":0.7651403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01517513158848686,"score_gpt":0.2480100157413196,"score_spread":0.2328348841528327,"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."}}