Bibliographic record
Abstract
The Qinghai Tibet Railway is a great engineering project and also constitutes a major part in the development of western China, thus becoming one of the focuses of world attention. Construction of the railway is of great significance. The railway is planned to pass through an average height of 4 500～5 000 m above sea level and has a total length of 1 110 km, with several tunnels more than 11 km in total length to be excavated. One of them is the Kunlun Mountain tunnel over 1 600 m long surrounded by rocks of low quality. A study of the tunnel stability is very important to safety. On the basis of geological structural analysis, rock stress measurement, rock mechanical experimentation and inversion of tectonic stress field, this paper calculated stress distribution of tunnels in different sections by using finite element method. Stress pattern and ratio of rock strength to stress are given. Optimization of sections is made. It is shown that a tunnel which has curve sides and an arch bottom with ratio 1.08 of width to height is more stable than other types. Scientific basis is provided for reasonable designing, construction and long term stability of the tunnel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".