{"id":"W2142639122","doi":"10.1145/2069000.2069011","title":"Cognitive wireless sensor networks for highway safety","year":2011,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Computer science; Perspective (graphical); Cognition; Cognitive network; Cognitive radio; Context (archaeology); Human–computer interaction; Key distribution in wireless sensor networks; Wireless; Wireless network; Work (physics); Computer network; Engineering; Telecommunications; Artificial intelligence","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":[0.0006121618,0.000430961,0.0003403948,0.0004038627,0.0004784201,0.001066252,0.0008333129,0.001162202,0.003533097],"category_scores_gemma":[0.001276031,0.0001218681,0.0002699096,0.0007744107,0.0007872172,0.002058627,0.0008984695,0.001410399,0.0006537174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00058786,"about_ca_system_score_gemma":0.0006541188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001109203,"about_ca_topic_score_gemma":0.001643158,"domain_scores_codex":[0.9996899,0.0001070555,0.00001198568,0.00004256111,0.0001237183,0.00002477749],"domain_scores_gemma":[0.9996186,0.0001805951,0.00003700836,0.00004883343,0.0000860096,0.00002874001],"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":[0.00005323458,0.00003012407,0.0003297056,0.0007417903,0.00004786593,0.000188525,0.0001936763,0.02921809,0.004341896,0.7071281,0.02395572,0.2337713],"study_design_scores_gemma":[0.00002051751,0.00007716424,0.0003876026,0.0002511507,0.00003223188,0.0002556159,0.0002160575,0.06074238,0.001162653,0.6217975,0.3150235,0.00003368842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008614953,0.1259743,0.7432199,0.02339062,0.004188882,0.0001123085,0.0001856692,0.0005441929,0.09376914],"genre_scores_gemma":[0.5208066,0.1501935,0.2760643,0.005656008,0.005304928,0.0003842367,0.0003917972,0.0001020043,0.04109668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003533097,"threshold_uncertainty_score":0.01181936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02736622729857865,"score_gpt":0.232290929741152,"score_spread":0.2049247024425733,"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."}}