{"id":"W2073136791","doi":"10.1007/s10291-006-0024-x","title":"Optimal linear combinations of triple frequency carrier phase data from future global navigation satellite systems","year":2006,"lang":"en","type":"article","venue":"GPS Solutions","topic":"GNSS positioning and interference","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Computer science; Multipath propagation; Galileo (satellite navigation); Global Positioning System; Satellite; Satellite system; Multipath mitigation; Communications satellite; GLONASS; Algorithm; Remote sensing; Telecommunications; Engineering; Geography","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":[0.0008843523,0.001149478,0.0006561452,0.0006370937,0.0004429458,0.001508006,0.0005897012,0.0007763851,0.006190217],"category_scores_gemma":[0.002640836,0.0005847349,0.0005346065,0.001296231,0.0005019425,0.001323547,0.000800497,0.0006830791,0.001539787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005166092,"about_ca_system_score_gemma":0.0010684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009280899,"about_ca_topic_score_gemma":0.002459653,"domain_scores_codex":[0.9990375,0.0002471971,0.00004602525,0.0001264905,0.0003689541,0.0001739518],"domain_scores_gemma":[0.9992912,0.0002843266,0.00007085689,0.00008476587,0.0002214758,0.00004735321],"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":[0.001929768,0.0001541566,0.002171271,0.0002862005,0.0001601974,0.0002419127,0.0001617565,0.3997807,0.04530123,0.03393297,0.006165077,0.5097147],"study_design_scores_gemma":[0.0001045973,0.0006682907,0.003503787,0.0001241503,0.0002498988,0.0005334766,0.0002279998,0.8979488,0.05477163,0.02760604,0.01414296,0.0001182806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1703341,0.001614924,0.7955337,0.000712369,0.0003295018,0.00009048494,0.0007276558,0.0005045502,0.03015277],"genre_scores_gemma":[0.717526,0.001383319,0.2664279,0.0002020227,0.0002445466,0.0001467059,0.001060987,0.0001685492,0.01284002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006190217,"threshold_uncertainty_score":0.02070832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02778537961601409,"score_gpt":0.2727843357388567,"score_spread":0.2449989561228426,"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."}}