{"id":"W1992558361","doi":"10.3390/s131115221","title":"A New Approach for Improving Reliability of Personal Navigation Devices under Harsh GNSS Signal Conditions","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Unavailability; Computer science; Reliability (semiconductor); GNSS augmentation; Multipath propagation; SIGNAL (programming language); Real-time computing; Satellite navigation; Multipath mitigation; Satellite system; Global Positioning System; Remote sensing; Reliability engineering; Engineering; Telecommunications; Channel (broadcasting)","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":[0.00005778878,0.00008056888,0.00009596403,0.00003094335,0.00005102871,0.00002952495,0.00005595622,0.00005532681,0.0001387781],"category_scores_gemma":[0.00001398911,0.00007942816,0.0000543244,0.00005881803,0.00003047818,0.0001384384,0.000006532177,0.00008243801,0.00002275453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003693509,"about_ca_system_score_gemma":0.00001857428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002450584,"about_ca_topic_score_gemma":0.000002752614,"domain_scores_codex":[0.9995056,0.00001311784,0.0001497239,0.0001242742,0.00007768153,0.000129549],"domain_scores_gemma":[0.9996847,0.00005857417,0.0000318113,0.00008508123,0.0000888092,0.00005101638],"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":[0.00003376453,0.0001770902,0.003086004,0.001969732,0.000164351,4.064079e-7,0.006053132,0.6890824,0.2778493,0.003229646,0.01116953,0.007184595],"study_design_scores_gemma":[0.0002481565,0.00005218971,0.008432279,0.0000585348,0.00002845639,0.000005033046,0.0008729415,0.9688786,0.02009032,0.001133769,0.00004091101,0.0001588278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9109731,0.00001827241,0.0864985,0.00003720988,0.00006482549,0.0002123586,0.00003010467,0.00009414728,0.002071532],"genre_scores_gemma":[0.9879042,4.09859e-7,0.01161932,0.0000137041,0.0000526741,0.00003044042,0.00007517818,0.00001283061,0.0002912454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2797961,"threshold_uncertainty_score":0.3238986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01506919788207783,"score_gpt":0.2317464700963986,"score_spread":0.2166772722143208,"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."}}