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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".