{"id":"W4385989006","doi":"10.1016/j.heliyon.2023.e19254","title":"Head impact kinematics and injury risks during E-scooter collisions against a curb","year":2023,"lang":"en","type":"article","venue":"Heliyon","topic":"Automotive and Human Injury Biomechanics","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hybrid III; Kinematics; Acceleration; Crashworthiness; Collision; Crash; Concussion; Angular acceleration; Poison control; Automotive engineering; Head (geology); Simulation; Physical medicine and rehabilitation; Computer science; Environmental science; Engineering; Injury prevention; Physics; Medicine; Geology; Emergency medicine; Computer security","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":[0.0001926644,0.000288858,0.0002765958,0.0004997777,0.0001654577,0.0002972242,0.0002286586,0.0004941346,0.00149087],"category_scores_gemma":[0.001036726,0.0002450929,0.0003181406,0.0002005382,0.0002185032,0.0002786602,0.0003710181,0.0001263021,0.0002107066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001969747,"about_ca_system_score_gemma":0.000193176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00721798,"about_ca_topic_score_gemma":0.009399042,"domain_scores_codex":[0.9998798,0.00002707837,0.00001042631,0.00001946918,0.00003326329,0.00003002057],"domain_scores_gemma":[0.9997225,0.0001048444,0.00008433879,0.00002115713,0.00003836918,0.00002863662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002073858,0.0005797268,0.746839,0.0001082094,0.0002002422,0.001845199,0.0003480124,0.1954956,0.03082733,0.0001381968,0.0002427837,0.02130178],"study_design_scores_gemma":[0.0000556148,0.002561835,0.7833049,0.00003122708,0.0000724764,0.001197552,0.0009347683,0.2037199,0.007572552,0.0002986994,0.000214997,0.00003542824],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999483,0.00001473235,0.0003478187,0.000003593942,5.731162e-7,0.000005127974,0.00003848318,0.000008898876,0.00009766613],"genre_scores_gemma":[0.9996371,0.00001954334,0.0001427763,0.000002228203,4.298651e-7,0.000002674634,0.00007346999,0.000001559975,0.0001201697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00721798,"threshold_uncertainty_score":0.0143519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05315839659713321,"score_gpt":0.3778426484314913,"score_spread":0.3246842518343581,"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."}}