{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000302222,0.00005609274,0.00007337306,0.00008615003,0.00005501046,0.00003374528,0.00003624088,0.00006272669,0.000005759762],"category_scores_gemma":[0.00001613258,0.00005577531,0.00004452596,0.0001351782,0.00001079621,0.0003002073,0.000002678114,0.0002477598,0.000001512122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001220704,"about_ca_system_score_gemma":0.000006031265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004806277,"about_ca_topic_score_gemma":0.000001577118,"domain_scores_codex":[0.9993927,0.0000136119,0.000327209,0.00003779865,0.0001280915,0.0001006291],"domain_scores_gemma":[0.9996563,0.00002510958,0.000115618,0.00003710269,0.0001241415,0.00004177667],"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":[0.00003250624,0.0000146475,0.0009164852,0.000005418298,0.00001308619,0.000004627187,0.0001259187,0.9509454,0.02566927,0.00004350202,0.0003410793,0.02188805],"study_design_scores_gemma":[0.00008798322,0.00009527708,0.007866724,0.0001135978,0.00002129936,0.0002875658,0.00007154604,0.9788748,0.01235514,0.0001351143,0.0000422929,0.00004865999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6003946,0.00004959376,0.3960432,0.000005378985,0.003217112,0.00001801044,3.792504e-7,0.00002862812,0.0002431036],"genre_scores_gemma":[0.9985272,0.000005494986,0.0004587246,0.000007381209,0.0009806938,1.741976e-7,0.000005404083,0.000008959312,0.000005938437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3981326,"threshold_uncertainty_score":0.2274451,"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."}}