Use of Economic and Mathematical Modeling Tools in Planning Investments in Fixed Assets
Bibliographic record
Abstract
In order to maximize the effectiveness of fixed assets use it is necessary to assess the impact of organizational factors on capital productivity of fixed assets, and also to assess the feasibility of capital investment in fixed assets. The purposes of the study are to design an economic-mathematical model that makes it possible to predict a value of capital productivity knowing the values of different factors, as well as to calculate the effectiveness of capital investment in fixed assets on the example of the regional branch of Tatarstan Energy Company. During the correlation and regression analysis of the Tatarstan energy company branch, the authors found that the cost of the active part of fixed assets has the greatest impact on the capital productivity of fixed assets, so the company is recommended to increase the active part of fixed assets. The proposed approach to scenario forecasting of capital investments in fixed assets allows to assess the prospects for changes in the company's financial performance as a result indicator of the company's performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".