{"id":"W2108631995","doi":"10.1017/s0373463307004158","title":"DGPS Correction Prediction Using Artificial Neural Networks","year":2007,"lang":"en","type":"article","venue":"Journal of Navigation","topic":"GNSS positioning and interference","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial neural network; Computer science; Pseudorange; Feedforward neural network; Feed forward; Data pre-processing; Preprocessor; Probabilistic neural network; Differential GPS; Global Positioning System; Data mining; Artificial intelligence; Time delay neural network; MATLAB; Data assimilation; Machine learning; GNSS applications; Control engineering; Engineering","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":[0.0004462852,0.0006140127,0.0005035609,0.0004990641,0.0002303307,0.0006277604,0.0005609684,0.0006697812,0.0007508626],"category_scores_gemma":[0.002348768,0.0003438164,0.0003575802,0.0006843846,0.0002228751,0.0007794356,0.000251804,0.0005600677,0.000238557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007793765,"about_ca_system_score_gemma":0.0007044594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02588475,"about_ca_topic_score_gemma":0.01853194,"domain_scores_codex":[0.9997944,0.00004490432,0.00001579597,0.0000564825,0.00006193683,0.00002639572],"domain_scores_gemma":[0.9993756,0.0003339289,0.00006981844,0.00003191285,0.0001756369,0.0000131786],"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":[0.00002052597,0.00001092783,0.0008938008,0.00001047707,0.00001143774,0.00001271132,0.000006579995,0.9861523,0.0003754059,0.0001885522,0.000106693,0.01221055],"study_design_scores_gemma":[6.621953e-7,0.000002809062,0.0001318868,9.545156e-7,0.000001149833,0.000001218216,8.481148e-7,0.9996136,0.0001286896,0.00009298371,0.00002403178,0.000001163392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3260636,0.0005757599,0.6675217,0.0002391822,0.00007409691,0.00004003997,0.0002701598,0.001487394,0.003728023],"genre_scores_gemma":[0.9671829,0.0001881835,0.03053258,0.00002295471,0.00001623604,0.00004201962,0.0002061158,0.00002218393,0.001786707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02588475,"threshold_uncertainty_score":0.05146813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01704531642699735,"score_gpt":0.2488777300217483,"score_spread":0.231832413594751,"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."}}