Modeling and Forecasting of Provision of Energy Security of Ukraine with Energy Resources
Bibliographic record
Abstract
The article proposes to use the models of provision of the energy security of Ukraine with energy resources in the context of globalization as a descriptive-model representation of the process for describing, analyzing, evaluating, managing and forecasting the state's fuel and energy resources (FER).It has been determined that the shortage of energy resources is mainly caused by their irrational use by all market entities, as well as their noticeable decrease in production within the state.Their import from abroad leads to a weakening of the position of Ukraine's energy independence in the international market.A system of functional dependencies of individual FER determinants is proposed.It has been established that the path to energy independence of Ukraine is impossible without careful and rational use of resources.It has been established that the modeling process acts as a kind of tool for the authorities in shaping energy policy in Ukraine.A model of an organizational mechanism for ensuring the efficiency of the use of fuel and energy resources at various hierarchical levels is proposed, where subjects of regulatory and supervisory activities are represented within each level, their functional relationships and influence are regulated by the current legislation.Forecasting and assessment of energy independence of the national economy of Ukraine until 2035 was carried out.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".