{"id":"W3027679662","doi":"10.1177/1550147720920478","title":"An adaptive method based on contextual anomaly detection in Internet of Things through wireless sensor networks","year":2020,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Anomaly detection; Anomaly (physics); Wireless sensor network; Data mining; The Internet; Wireless; Computer network; Telecommunications; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005226957,0.0005768434,0.0007922677,0.001174832,0.0005828644,0.0005165179,0.001132257,0.0006159052,0.0004259559],"category_scores_gemma":[0.002396685,0.0002352632,0.0006594418,0.0009775483,0.0004550586,0.0009085598,0.0007624334,0.0007095794,0.0001368758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003749595,"about_ca_system_score_gemma":0.0006327734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003315216,"about_ca_topic_score_gemma":0.004048422,"domain_scores_codex":[0.9991875,0.0001249402,0.00004804442,0.0002747311,0.0002977747,0.0000670609],"domain_scores_gemma":[0.9992455,0.0002352024,0.0001040498,0.00009645567,0.00027876,0.00004015222],"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":[0.000196315,0.0002205855,0.009153645,0.0001808946,0.0001772528,0.0005338446,0.0002711039,0.2276947,0.04049002,0.01337337,0.003622096,0.7040862],"study_design_scores_gemma":[0.000006384854,0.00003893186,0.001021086,0.000004752411,0.00002496638,0.000177628,0.00002583975,0.9907375,0.004190771,0.002268201,0.001488718,0.00001516681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01981263,0.0002059415,0.9784356,0.000103442,0.00008879958,0.00005341342,0.00002775937,0.0006383776,0.00063393],"genre_scores_gemma":[0.6007609,0.0004213675,0.3961932,0.0001895386,0.0001853962,0.0001401585,0.000188093,0.0001144056,0.001806883],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003315216,"threshold_uncertainty_score":0.006591856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01987025515179847,"score_gpt":0.2831473270530145,"score_spread":0.263277071901216,"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."}}