Methodological Approach to Planning and Financing the Fixed Capital Reproduction for Sustainable Development of the Fishing Industry
Bibliographic record
Abstract
The article presents the results of scientific and research work on the creation of a methodological approach to the analysis of fixed capital reproduction for fishing enterprises. This study is relevant due to the fact that the fishery complex of the Russian Federation is currently called upon to provide a solution to many political and economic problems, which, in particular, include transition to an innovative type of industrial production, provision of food security, as well as maintenance of a favorable state of aquatic biological resources. Due to this industry's high capital intensity, the issues of choosing a methodology that make it possible to rationally control the fixed capital reproduction of fishing enterprises become especially important, which is likely based on an analysis of existing methods that allow planning these processes. The advantages of the developed methodological approach include the establishment of uniform methodological principles used to determine the economic efficiency of investments, new equipment, inventions and rationalization proposals, more accurate consideration of the time factor concerning the determination of the integral economic effect (for the entire service life of labor means), as well as the factor time by bringing one-time and ongoing costs for the creation and implementation of new and necessary equipment and the results of their application to one point in time (the beginning of the accounting year). The article includes the main advantages and disadvantages of financing the reproduction processes possible ways.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".