{"id":"W4399412985","doi":"10.1109/jiot.2024.3409386","title":"CPBW: A Change-Point-Detection and Bag-of-Words-Based Mechanism Utilizing Smartphone Triaxial Accelerometer Data for Driver Identification","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"National Science and Technology Council; Ministry of Science and Technology, Taiwan; Ministry of Higher Education","keywords":"Accelerometer; Computer science; Identification (biology); Mechanism (biology); Point (geometry); Real-time computing; Embedded system; Operating system","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":[0.003499072,0.0001476564,0.0002792153,0.0004081092,0.00006668305,0.0004456884,0.0009689199,0.00009139589,0.00001142621],"category_scores_gemma":[0.0002671795,0.0001300664,0.0001347266,0.0002599739,0.00004631152,0.002044624,0.0001669897,0.0002799951,0.000002622698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000422714,"about_ca_system_score_gemma":0.0000656413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005863617,"about_ca_topic_score_gemma":0.00001308137,"domain_scores_codex":[0.9983191,0.0001567533,0.0005865374,0.0004107019,0.0003184207,0.000208417],"domain_scores_gemma":[0.9984556,0.0004434922,0.0003962973,0.0004681044,0.0001650446,0.00007148527],"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":[0.0001931422,0.00007465649,0.00008519599,0.0004102094,0.0002101763,0.00002060344,0.00397733,0.000008529834,0.3125759,0.001196348,0.000409411,0.6808385],"study_design_scores_gemma":[0.001210489,0.0005030098,0.001128652,0.0008667172,0.00008491258,0.0002510458,0.00006313915,0.4726923,0.505919,0.01584376,0.001166642,0.0002702428],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1286969,0.0003351926,0.8663833,0.0003448874,0.003977031,0.0001840433,0.00001105616,0.00005514778,0.00001246295],"genre_scores_gemma":[0.9277903,0.0000643774,0.07165675,0.00009140276,0.0003258741,0.0000105924,0.000003858896,0.00001727015,0.00003961373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7990934,"threshold_uncertainty_score":0.5303952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1158972989125812,"score_gpt":0.3486824884422209,"score_spread":0.2327851895296398,"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."}}