{"id":"W2168700653","doi":"10.1109/cimsa.2003.1227198","title":"Evolutionary neural network-based sensor self-calibration scheme using IEEE 1451 and wireless sensor networks","year":2004,"lang":"en","type":"article","venue":"","topic":"Sensor Technology and Measurement Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Wireless sensor network; Computer science; Key distribution in wireless sensor networks; Scalability; Intelligent sensor; Visual sensor network; Artificial neural network; Real-time computing; Plug and play; Interface (matter); Scheme (mathematics); Mobile wireless sensor network; Wireless; Embedded system; Computer network; Wireless network; Artificial intelligence; Telecommunications","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":[0.000342587,0.0002369563,0.0002346752,0.0001141944,0.0004307875,0.0001051146,0.0003152015,0.0002715455,0.000004438409],"category_scores_gemma":[0.00001443841,0.0002227861,0.00007935325,0.0004980402,0.00009231328,0.0004934398,0.00003613514,0.0002968167,0.000007183208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001160013,"about_ca_system_score_gemma":0.00008882283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005300907,"about_ca_topic_score_gemma":0.00001311597,"domain_scores_codex":[0.9981689,0.000156236,0.0003327385,0.0005391042,0.0002981539,0.0005048449],"domain_scores_gemma":[0.9990666,0.00007701558,0.0001374452,0.0004965064,0.0001040341,0.0001183952],"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":[0.00004381755,0.0002092039,0.02258179,0.00005454685,0.0001182014,0.00009694285,0.0001399402,0.8913278,0.06207339,0.02160164,0.000833729,0.0009190075],"study_design_scores_gemma":[0.0008334009,0.00006125915,0.001455654,0.00004545715,0.00001658683,0.000131931,0.00002246997,0.9932569,0.0035638,0.0002351264,0.00009713468,0.0002803298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4122878,0.0001679004,0.5849622,0.0007628312,0.0006406817,0.0002657541,4.98606e-7,0.000833431,0.00007888766],"genre_scores_gemma":[0.8420775,0.000007271654,0.1568975,0.0006700534,0.0002841851,0.000008717418,0.000002726422,0.00001542389,0.00003659612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4297897,"threshold_uncertainty_score":0.9084951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02601883197399178,"score_gpt":0.2304924428380722,"score_spread":0.2044736108640804,"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."}}