Bibliographic record
Abstract
This article presents key aspects of the logistics and equipment choices that will most efficiently remove muck and provide other sorts of support to tunneling projects, especially as more and more projects are undertaken in crowded, urban settings or locales with fragile natural environments. Several prominent tunnels require diametrically opposite treatments. For example, the Second Avenue Subway in New York City requires trucks to operate only during waking hours to avoid disturbing residents as the trucks haul away muck and materials for the project. By contrast, in Hong Kong, trucks were banned from city streets during daylight hours to reduce congestion and conflicts with regular traffic. Depending on the locale and the project specifications, muck and materials can be supplied by rail, truck or conveyor belt. Some combine two or even all three. Specialist equipment makers fabricate machines to meet production requirements and the physical constraints of the setting. Such customized equipment includes a custom-built train that will be used for maintenance and other work when the tunnel is completed. Heavy-duty slurry treatment plants must have enough capacity to avoid forcing the tunneling equipment to slow down. Showcase projects include London’s Crossrail scheme, Vancouver’s new Canada Line, and Kowloon Southern Link, in Hong Kong.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".