{"id":"W2051062192","doi":"10.1109/glocom.2009.5425997","title":"Multiple-Symbol Differential Decision Fusion for Mobile Wireless Sensor Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Fusion center; Fading; Computer science; Fusion rules; Symbol (formal); Channel (broadcasting); Fusion; Overhead (engineering); Wireless sensor network; Wireless; Sensor fusion; Keying; Fusion mechanism; Algorithm; Decision rule; Real-time computing; Artificial intelligence; Telecommunications; Computer network; Cognitive radio","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001292968,0.0002162094,0.0002452405,0.00008458604,0.00029217,0.0002989961,0.0005039453,0.0001642424,0.00004808843],"category_scores_gemma":[0.00002499987,0.000176295,0.0001829772,0.0003647643,0.00001984547,0.0002515099,0.0001029936,0.0001486873,0.00002240703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003690673,"about_ca_system_score_gemma":0.00001420326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006694082,"about_ca_topic_score_gemma":0.000007479642,"domain_scores_codex":[0.9983443,0.00003383502,0.0003371909,0.0005396968,0.0002802826,0.0004647],"domain_scores_gemma":[0.9987435,0.0002970704,0.00009136224,0.0005230677,0.000156187,0.0001888416],"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":[0.00008880477,0.0001713764,0.00003055975,0.000002042003,0.000009207193,0.000005981611,0.00003278504,0.02461177,0.001049697,0.001625359,0.004624224,0.9677482],"study_design_scores_gemma":[0.001095586,0.0003445562,0.0009255834,0.00001565458,0.000006468913,0.00001391648,0.00002250459,0.9900484,0.001332508,0.0005941379,0.005347005,0.0002536807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06037723,0.00004218817,0.9369773,0.0001168857,0.001285845,0.000535358,0.000007201711,0.0004416301,0.0002164277],"genre_scores_gemma":[0.9469732,0.00003814624,0.05173169,0.0003624641,0.0004207192,0.00003730966,0.00002402907,0.00001124733,0.0004012094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9674945,"threshold_uncertainty_score":0.71891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008878318908635498,"score_gpt":0.2373877798960388,"score_spread":0.2285094609874034,"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."}}