{"id":"W4410525813","doi":"10.2196/71969","title":"Recognizing Skateboard and Kickboard Commuting Behaviors Using Activity Trackers: Feasibility Study Using Machine Learning Approaches","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Injury Epidemiology and Prevention","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; BitTorrent tracker; Computer science; Activity tracker; Human–computer interaction; Artificial intelligence; Machine learning; Embedded system; World Wide Web; Eye tracking","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001841524,0.0004925475,0.0003824532,0.0005809222,0.0002238166,0.0004590424,0.0003877314,0.0006317908,0.0009637649],"category_scores_gemma":[0.003621627,0.0002198395,0.0003647004,0.0002967528,0.0002375039,0.0007425538,0.0003505492,0.0003182219,0.0004465737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002249176,"about_ca_system_score_gemma":0.0005085625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002146253,"about_ca_topic_score_gemma":0.002841006,"domain_scores_codex":[0.9991434,0.0003624485,0.00006228031,0.0001852783,0.0001594388,0.00008716959],"domain_scores_gemma":[0.9981622,0.0008623429,0.0001521394,0.00008019157,0.0006057743,0.0001373226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004174738,0.01049491,0.6327814,0.0004672445,0.000243431,0.0002762593,0.0007482063,0.007426971,0.05558724,0.0001950755,0.0006211157,0.2869833],"study_design_scores_gemma":[0.0008313924,0.03249896,0.6217114,0.0001207575,0.0003862481,0.000765637,0.001571355,0.3166242,0.02340553,0.0004261759,0.001566912,0.00009140081],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9822257,0.00008328442,0.01666162,0.00006376662,0.00001397628,0.0003532887,0.00009287382,0.00005367952,0.0004518047],"genre_scores_gemma":[0.9730829,0.00009930314,0.02580214,0.00004247752,0.00001741423,0.0003639118,0.0002086453,0.000004996222,0.0003782545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002146253,"threshold_uncertainty_score":0.009739041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.357084237455698,"score_gpt":0.5177665164924767,"score_spread":0.1606822790367788,"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."}}