{"id":"W2509939652","doi":"10.1109/ivs.2016.7535444","title":"Manoeuvre segmentation using smartphone sensors","year":2016,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Principal component analysis; Artificial intelligence; Computer science; Support vector machine; Classifier (UML); Pattern recognition (psychology); Sliding window protocol; Segmentation; Dimensionality reduction; Mobile phone; Feature extraction; Computer vision; Window (computing)","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":[0.00003797711,0.00006262187,0.00005879219,0.00004100259,0.00003232232,0.000002942617,0.00004288204,0.00007442007,0.0003618456],"category_scores_gemma":[0.000002781996,0.0000436681,0.00001792998,0.00005582965,0.00002527461,0.00008209104,0.0000105824,0.00003681253,0.0002236027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005772886,"about_ca_system_score_gemma":0.000003706796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003229896,"about_ca_topic_score_gemma":0.000004598441,"domain_scores_codex":[0.9996725,0.000005079006,0.00008956497,0.00007221244,0.00003542478,0.0001252444],"domain_scores_gemma":[0.9998448,0.00001493555,0.000008508407,0.0001050541,0.000007136298,0.00001956247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008726087,0.00001441949,0.01051399,0.0000186058,0.00006328827,0.00001299586,0.0001344056,0.003989487,0.8429641,0.003604546,0.001033958,0.1376415],"study_design_scores_gemma":[0.001157875,0.00003500151,0.01933404,0.00003945797,0.00002595149,0.00005841139,0.0002149262,0.04883513,0.9229103,0.002010639,0.004870111,0.0005082108],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9118794,0.00002285845,0.0817249,0.0001433349,0.0001215888,0.00004487827,0.000001911866,0.000818391,0.005242705],"genre_scores_gemma":[0.9941472,0.00002032987,0.004927495,0.0000227912,0.00002173222,0.000002313955,6.793928e-7,0.00001287886,0.0008445652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1371333,"threshold_uncertainty_score":0.3961955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01011274612093693,"score_gpt":0.20491617810393,"score_spread":0.1948034319829931,"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."}}