{"id":"W2952362763","doi":"10.21608/iceeng.2010.33282","title":"Wavelet Spectral Techniques for GPS Errors Reduction","year":2010,"lang":"en","type":"article","venue":"The International Conference on Electrical Engineering/The International Conference on Electrical Engineering ","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Global Positioning System; Wavelet; Reduction (mathematics); Computer science; Remote sensing; Environmental science; Geodesy; Artificial intelligence; Geography; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006620993,0.0008481963,0.0005467482,0.001331084,0.0002858758,0.0007324775,0.000511283,0.0006299618,0.003272819],"category_scores_gemma":[0.001717398,0.0003150937,0.0006747266,0.002263567,0.0003121851,0.001028795,0.0007703057,0.001231225,0.001717141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002873391,"about_ca_system_score_gemma":0.0003152436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009515751,"about_ca_topic_score_gemma":0.0007158011,"domain_scores_codex":[0.9994435,0.00009651811,0.00002927821,0.00006988719,0.0003222641,0.00003856741],"domain_scores_gemma":[0.9995173,0.0001520351,0.00005695517,0.00009953773,0.0001605177,0.00001361464],"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":[0.000144009,0.0000744209,0.000620268,0.0002746782,0.00006051756,0.0001404855,0.0001655175,0.05438054,0.05580355,0.02848866,0.005267684,0.8545797],"study_design_scores_gemma":[0.00003697028,0.0001609133,0.003144549,0.0001143432,0.00006603159,0.000354495,0.0001617238,0.8605059,0.04208595,0.03787227,0.05543017,0.00006659017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00632792,0.001460178,0.9897911,0.0001518524,0.0000930809,0.00002748382,0.0001061149,0.0004402099,0.001602168],"genre_scores_gemma":[0.1561713,0.007369975,0.8250865,0.0001401896,0.0003563722,0.0001584635,0.001004901,0.0004074929,0.00930488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003272819,"threshold_uncertainty_score":0.01094872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0205156629101613,"score_gpt":0.2585068899142727,"score_spread":0.2379912270041114,"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."}}