Le projet de chemin de fer Chine-Kirghizstan-Ouzbékistan : défis et perspectives dans le cadre des nouvelles routes de la soie
Bibliographic record
Abstract
Soumission à Epi-revel Dans le cadre des nouvelles routes de la soie, lancées en 2013, la Chine négocie avec le Kirghizstan un projet de construction de voie ferrée à travers le territoire kirghiz vers l’Ouzbékistan, ouvrant ainsi une nouvelle voie reliant le Xinjiang à l’Asie centrale et au-delà vers le Moyen-Orient. Ce projet est en réalité discuté depuis près de vingt ans, et bute sur deux obstacles majeurs : le coût considérable d’un tel projet à travers des massifs montagneux, et le choix du tracé, que le Kirghizstan veut optimal pour son économie. Le gouvernement kirghiz résiste aux pressions chinoises pour le moment International audience As part of the new Silk Roads, initiated in 2013, China is negotiating with Kyrgyzstan a project to build a railway through Kyrgyz territory to Uzbekistan, thus opening a new route linking Xinjiang to Central Asia, and beyond to the Middle East. This project has in fact been discussed for nearly twenty years, and comes up against two major obstacles: the considerable cost of such a project to be built through mountain ranges, and the choice of the route, which Kyrgyzstan wants optimal for its economy. The Kyrgyz government is resisting Chinese pressure for the time being.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".