The Research on Stability of the Russian Banking System by Machine Learning Methods
Bibliographic record
Abstract
For the last few years, roads and motorways projects have been developing in Algeria, where the risk management remains timorous. Indeed, these projects exhibit many geotechnical disorders of natural or anthropic origin, which disturb their progress. Furthermore, the latent effects of these disorders can affect the life of the works as well as the safety and comfort of the users. Therefore, this paper addresses the use of the MADS-MOSAR method as a risk assessment method that can be used in the construction area to consider a wide range of risk sources including non-geotechnical ones. This method, which is widely used in industry, is tentatively used to identify the causes and consequences of undesired geotechnical events in the case of the main slip road “A” at the exchange of the RN02 road in Tlemcen, Algeria. The choice of this method implies first a system modelling approach and a functional analysis to inventory all possible sources of danger and all possible interactions and to perform a global risk assessment. At the end, we show the relevance of this method in the field of geotechnical risk management for road projects.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".