{"id":"W2902751724","doi":"10.1177/1550147718815798","title":"A novel time difference of arrival localization algorithm using a neural network ensemble model","year":2018,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Government of Jiangsu Province; Qinglan Project of Jiangsu Province of China; National Science Foundation","keywords":"Computer science; Artificial neural network; Generalization; Algorithm; Stability (learning theory); Time delay neural network; Arrival time; Probabilistic neural network; Artificial intelligence; Machine learning; Mathematics","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":[0.0007586003,0.0008024712,0.001031055,0.0006240044,0.0004691088,0.0007967079,0.00169973,0.0009872128,0.001232156],"category_scores_gemma":[0.001719912,0.0003872023,0.0008868635,0.000930806,0.0002885712,0.001553927,0.001010078,0.001483898,0.0004579115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006571415,"about_ca_system_score_gemma":0.0008834436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00933713,"about_ca_topic_score_gemma":0.005654026,"domain_scores_codex":[0.999615,0.00007834267,0.00002730502,0.0001053894,0.0001256793,0.00004827054],"domain_scores_gemma":[0.9994735,0.0001583137,0.00005873483,0.00003736732,0.0002411881,0.00003096643],"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":[0.00009337298,0.00004537401,0.00143529,0.00005642623,0.00008872369,0.00006131467,0.00006988312,0.7918707,0.003060461,0.005437368,0.001777532,0.1960036],"study_design_scores_gemma":[0.000002651427,0.00001040034,0.00006175954,0.000002057126,0.000006172129,0.000009104495,0.000002502848,0.9989207,0.0002581231,0.0004501869,0.0002735282,0.000002970352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007246267,0.0002404347,0.9911296,0.0001077002,0.00005401314,0.00001695218,0.0000258707,0.0002908955,0.0008881796],"genre_scores_gemma":[0.583617,0.0008792387,0.406788,0.0002510107,0.0001826146,0.0002679301,0.0003761547,0.0001329109,0.007505183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00933713,"threshold_uncertainty_score":0.01856554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01422031262926652,"score_gpt":0.237428626104626,"score_spread":0.2232083134753594,"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."}}