Improvement of the mechanism reducing the risks of financing of the investment projects
Bibliographic record
Abstract
© 2014, Canadian Center of Science and Education. All rights reserved. The aim of this work is the development of a mechanism for minimizing the risks of project financing. The article offers a methodology to reduce potential risks of financing investment projects. Methodology includes such basic steps as a sensitivity analysis of the project's net present value to changes in key financial and economic parameters of its realization. The method used is based on the scenario approach: expert evaluation of the relevance of project-specific risks, calculation of integrated risk evaluation and development of recommendations on the prevention of the most significant for the particular variant of project financing risk. In General, the proposed method allows, on the basis of the sensitivity analysis and synthesis expert estimation, highlight the most significant risks of project finance and develop activities to minimize them in the future. The proposed methodology has been tested on real production development project financing Ltd. "Lesokombinat"(all names changed in the article). Found that the most significant risks of project funding are possible decrease in operating income and an increase in operating costs. In the minimization of the risk of a possible reduction of the operating income includes the following main activities: active work with major customers; long-term contracts for the supply of woodworking products at fixed prices; a more active market research (now the plant practically not engaged in active market research and forecasting market size); strengthening participation in State and municipal order as potentially effective channel of saling of products of plant.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".