{"id":"W2991205601","doi":"10.1109/jiot.2019.2954720","title":"Decision Fusion for IoT-Based Wireless Sensor Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Fusion center; Wireless sensor network; Sensor fusion; Algorithm; Probability of error; Fusion rules; Fusion; Expression (computer science); Monte Carlo method; Channel (broadcasting); Wireless; Probability distribution; Artificial intelligence; Mathematics; Cognitive radio; Telecommunications; Computer network; Statistics","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":[0.00166448,0.000584184,0.0007369442,0.0004668494,0.000582343,0.0009313595,0.000849427,0.0007277403,0.0009226767],"category_scores_gemma":[0.004376931,0.0002496209,0.0004510474,0.0009123672,0.0007004201,0.001389079,0.00102415,0.001161777,0.0002394075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009548649,"about_ca_system_score_gemma":0.0008029913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00167359,"about_ca_topic_score_gemma":0.001643104,"domain_scores_codex":[0.9987417,0.0004423365,0.00007930074,0.000172208,0.0004893857,0.00007521115],"domain_scores_gemma":[0.9989692,0.0006650863,0.00009729955,0.00007757026,0.0001704907,0.00002037853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001208742,0.00004944875,0.0005756563,0.0002045928,0.00009159384,0.0001162541,0.0001067474,0.7238433,0.003838305,0.08227467,0.002398412,0.1863801],"study_design_scores_gemma":[0.000005773641,0.0000279658,0.00009360383,0.00001164838,0.000009497271,0.00003200637,0.0000112335,0.9701641,0.001012407,0.02705774,0.001566306,0.000007676886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00391689,0.001114484,0.993223,0.0001837227,0.00008858388,0.00002341069,0.00002329926,0.000119942,0.001306775],"genre_scores_gemma":[0.7193918,0.002947522,0.2741253,0.0003151644,0.0003179137,0.0001536153,0.0001528948,0.00004968773,0.002545996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00167359,"threshold_uncertainty_score":0.008802712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009323783177212806,"score_gpt":0.2366165369679819,"score_spread":0.2272927537907691,"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."}}