Bibliographic record
Abstract
Although Russian tunneling technology became well known for the work on the Moscow Metro's deep bed-rock stations, more recent, lesser- known projects also present valuable insights. Since the collapse of the former USSR in 1991, the metro systems in six Russian cities have come under municipal ownership. Construction is no longer financed solely from the state, but from a mix of sources. In the years since, metro construction has gotten underway in five more cities. A total of some 4.2 billion passengers ride on a metro system each year. Tunnel manufacturers and suppliers have enjoyed success in the current Russian Federation as well as under the previous regime, with Lovat of Canada enjoying the most success in the country to date. The company has filled several major TBM orders to the extent that the Russian market has outperformed the North America, South America and Australia markets. Contractors have not been able to penetrate the market as easily, in large part due to Russia's lack of membership in the World Trade Organization. Bidding and pricing is guided by complex rules and the legal system is complex. In the meantime, the new climate of openness in the country is allowing home-grown experts to try out new approaches and innovate. An undersea rail tunnel used a combination of immersed tube and bored construction. More road tunnels are planned to deal with growing auto congestion, and demand for tunnels in other areas remains strong.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".