Factoring environment into electrification management in a region
Bibliographic record
Abstract
Electrification is an integrated continuous process of production, transmission, distribution and use of electric power in a region's economy.Analysis of global trends shows that demand for electric power as the most advanced and flexible energy carrier has been growing as many countries are in transition to a new industrial landscape.Along with that, the energy provision of industrial and domestic processes is becoming more intellect-intensive, while environmental issues are receiving special attention.This determines the relevance of the study.The authors have designed a methodology for factoring the environment into regional electrification programs.The methodology includes the following steps: ranking and selecting facilities to be electrified based on the criterion of minimal values of the 'electricity-for-fuel substitution coefficient'; application of the energy and economic effect to financially compensate for environmental impacts; introduction of demand side management programs that help improve the environmental situation in the region.An 'ideal' structural model of electrification is proposed for regions with high eco-loads.The model combines a method for selecting facilities to be electrified, pro-active energy conservation, adding more energy installations utilizing carbon-neutral fuels to the power generating system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".