{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031336,0.0002584317,0.0002781415,0.00009169982,0.0002339448,0.00008099149,0.0008161688,0.0001704252,0.00006406974],"category_scores_gemma":[0.00002879761,0.0002266389,0.0001492,0.0004213947,0.0001000316,0.0002941928,0.0002375382,0.000173908,0.00004956374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003837606,"about_ca_system_score_gemma":0.00003518655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004075455,"about_ca_topic_score_gemma":0.00003375474,"domain_scores_codex":[0.997998,0.0000853522,0.0003519939,0.0006668314,0.0002227855,0.0006750138],"domain_scores_gemma":[0.9983661,0.0005055421,0.0001295042,0.0005479553,0.0002651401,0.0001857137],"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":[0.0003281978,0.0005291824,0.0009484662,0.00002688959,0.0001896768,0.00006782272,0.001643787,0.0407803,0.0001696376,0.8226008,0.004045492,0.1286697],"study_design_scores_gemma":[0.0009732582,0.000168382,0.001189746,0.00004836297,0.0000191268,0.00001805016,0.0000860475,0.9904687,0.004226183,0.0002981891,0.002021824,0.0004821104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006894921,0.00005892507,0.9618597,0.0001157418,0.0009555464,0.000382126,0.000004660727,0.0006485261,0.02907985],"genre_scores_gemma":[0.8834234,0.00003029776,0.1133665,0.0007726697,0.0002209446,0.00005123688,0.00001305263,0.00003277622,0.002089136],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9496884,"threshold_uncertainty_score":0.9242065,"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."}}