Impact of High-Altitude on Truck’s Climbing Speed: Case study in Qinghai-Tibet Plateau Area in China
Bibliographic record
Abstract
Truck’s climbing performance is an important consideration in traffic safety, efficiency, and highway geometric design. With the infrastructure development in high-altitude area in China, more attention needs to be paid on truck’s climbing performance in such area. In this article, truck’s climbing speed in high-altitude area was examined through field tests on different grade sections at different altitudes. Truck’s speed-distance curves were built at different altitudes and the impact of altitude on truck’s climbing speed was explored based on the test results. It was shown that, within the altitude range of 3000~5000m, altitude had an obvious influence on test truck’s decelerating and accelerating performance. Truck’s speed decreased faster on steep grades and increased slower on gentle grades with the increase of the altitude. Also, the stable speed that test truck could maintain on a certain grade was lower at a higher altitude. In addition, test truck’s theoretical speed-distance curves at the sea level were estimated through truck’s dynamic model. Compared with the theoretical crawl speed, a negative effect of altitude change (from 0 to the altitude above 3000 m) was found on truck’s climbing performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".