{"id":"W4406060583","doi":"10.36227/techrxiv.173602799.91045327/v1","title":"Accurate Vehicle Maneuver Analysis Using Smartphone GNSS Carrier-Phase Measurements","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; China Scholarship Council","keywords":"GNSS applications; Computer science; Heading (navigation); Acceleration; Displacement (psychology); Angular velocity; Reduction (mathematics); Global Positioning System; Simulation; Real-time computing; Engineering; Aerospace engineering; Mathematics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000106529,0.0007660486,0.0003585298,0.001111246,0.0001481387,0.0003432748,0.0003666221,0.0003221621,0.0007121721],"category_scores_gemma":[0.0007164311,0.0001672053,0.0003166328,0.0006253401,0.0001717619,0.0005457914,0.0005590016,0.0003263636,0.000583973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001734523,"about_ca_system_score_gemma":0.0003123004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003791058,"about_ca_topic_score_gemma":0.005809882,"domain_scores_codex":[0.9997745,0.00002546616,0.000008773527,0.00005456235,0.0001054289,0.00003116325],"domain_scores_gemma":[0.9998373,0.00002545072,0.00002983341,0.00003766768,0.00005966452,0.00001005292],"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":[0.0002764903,0.0001057994,0.04208616,0.0002775894,0.0001434136,0.0003315204,0.000313622,0.1463164,0.1372734,0.002987172,0.004151513,0.665737],"study_design_scores_gemma":[0.0000222125,0.0001375148,0.0387928,0.00003082965,0.00004454877,0.0003622161,0.0002740599,0.9207371,0.03053329,0.003213541,0.005806085,0.0000457786],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2514847,0.0003886859,0.7396992,0.0001329078,0.0001088267,0.00007760419,0.0008612424,0.002835623,0.004411241],"genre_scores_gemma":[0.9094621,0.0002090914,0.0878061,0.00004234977,0.00004058016,0.00004220087,0.001117676,0.0001039591,0.001176029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003791058,"threshold_uncertainty_score":0.007538021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04772674717011116,"score_gpt":0.3031574877841882,"score_spread":0.255430740614077,"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."}}