{"id":"W4408483643","doi":"10.5194/egusphere-egu25-20749","title":"Investigating Galileo Signal Tracking Challenges in Smartphones","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Fractal and DNA sequence analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trusted Positioning (Canada); University of Calgary","funders":"","keywords":"Galileo (satellite navigation); SIGNAL (programming language); Tracking (education); Computer science; Aeronautics; Real-time computing; Remote sensing; Engineering; Geography; Psychology","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":[0.001348125,0.0005312901,0.000552026,0.0007688004,0.0006655221,0.002772961,0.0008277439,0.001053442,0.00192282],"category_scores_gemma":[0.007169954,0.00026378,0.0002850314,0.0009547694,0.0004521117,0.002703303,0.001487614,0.0008170989,0.001007512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006136,"about_ca_system_score_gemma":0.0006586764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01183819,"about_ca_topic_score_gemma":0.01077292,"domain_scores_codex":[0.9986064,0.0003404367,0.00007999087,0.0002301705,0.0004871134,0.0002558968],"domain_scores_gemma":[0.9960299,0.001692841,0.0003994739,0.0002853767,0.001363722,0.000228687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002067446,0.0006722262,0.2706612,0.004745776,0.0003321617,0.007244756,0.01160044,0.04561413,0.04389481,0.02644672,0.05342209,0.5332982],"study_design_scores_gemma":[0.0001217764,0.003233132,0.2527001,0.00164502,0.0004770227,0.009584164,0.03826723,0.4669831,0.03622263,0.01633518,0.1740146,0.0004159512],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9196597,0.01234498,0.03168612,0.005130592,0.0005322929,0.0004323256,0.001494737,0.0006225521,0.02809674],"genre_scores_gemma":[0.9821207,0.003952799,0.007964111,0.0005743184,0.0001064867,0.00007706525,0.0008348526,0.00004934956,0.004320267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01183819,"threshold_uncertainty_score":0.02353859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05455375110602006,"score_gpt":0.2923505684348707,"score_spread":0.2377968173288506,"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."}}