Experimental Study on Occupant Secondary Impact Injury for High-speed Trains
Bibliographic record
Abstract
摘要： 列车碰撞是我国伤亡最严重的重大列车安全事故之一，研建专业的列车乘员二次碰撞试验系统并开展列车乘员碰撞试验对科学再现乘员冲击动力学损伤响应、评估列车被动安全性具有重要意义。依托已有的轨道车辆实车碰撞试验系统，成功研建了列车乘员二次碰撞试验平台。基于50百分位Hybrid Ⅲ假人，开展了高速列车乘员二次碰撞试验，分析座椅靠背角度和可折叠小桌板对列车乘员碰撞损伤响应的影响。结果显示，在列车耐撞性标准规定的碰撞速度下，乘员头部加速度峰值最大为30g，但头部损伤准则(Head injury criteria,HIC)最大值只有17.38，头部损伤并不严重。打开小桌板时，乘员胸部损伤虽并不明显，但颈部撞击小桌板出现的“锁喉”现象可能会威胁乘员生命安全。试验数据可为我国高速列车内饰结构优化设计和列车内饰碰撞安全标准的制定提供重要数据参考。
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".