{"id":"W1978100560","doi":"10.1145/2386958.2386967","title":"A cognitive WSN framework for highway safety based on weighted cognitive maps and Q-learning","year":2012,"lang":"en","type":"article","venue":"","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Key (lock); Cognition; Protocol (science); Wireless sensor network; Warning system; Wireless; Base station; Cognitive network; Cognitive radio; Machine learning; Artificial intelligence; Computer network; Computer security; Telecommunications","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":[0.0009697429,0.0002217671,0.0002184003,0.0001969553,0.0005720895,0.0001745354,0.0002461602,0.000107863,0.00006888979],"category_scores_gemma":[0.001224304,0.0001869298,0.00008218992,0.0005369087,0.000138184,0.0007791962,0.0001708693,0.0003462586,0.0000912197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003167438,"about_ca_system_score_gemma":0.00007252921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008373306,"about_ca_topic_score_gemma":0.000002531215,"domain_scores_codex":[0.9981087,0.0001699367,0.0002042035,0.0005430101,0.0003145133,0.0006596503],"domain_scores_gemma":[0.9952197,0.003954154,0.0001049382,0.0001466223,0.0003085439,0.0002660054],"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":[0.0004191418,0.0004626982,0.01045984,0.00006921239,0.00008668922,0.00001115821,0.005767817,0.000006278089,0.0002503041,0.443583,0.0004267601,0.5384571],"study_design_scores_gemma":[0.01808541,0.005913584,0.2112725,0.005705833,0.0004208691,0.00008292945,0.01678721,0.5284986,0.04065444,0.1132817,0.05340855,0.005888369],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008419153,0.00006783519,0.9712915,0.0008733182,0.0003127499,0.0005733416,0.00002318942,0.0002375726,0.01820128],"genre_scores_gemma":[0.9310204,0.000009609656,0.06449599,0.003817027,0.0001554934,0.00008161055,0.00001962862,0.00001261915,0.0003875841],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9226013,"threshold_uncertainty_score":0.7622775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02371711963878098,"score_gpt":0.2809813680678205,"score_spread":0.2572642484290396,"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."}}