MODERN ECOLOGICAL AND ECONOMIC PROJECTS OF TRANSFORMATION OF COAL MINING REGIONS IN THE WORLD
Bibliographic record
Abstract
Given the environmental issues of coal mining regions and the transition to alternative fuels, the issue of transformation of mining regions is relevant. Coal mining regions affect not only landscape change, but also emissions of gases, including carbon dioxide. Coal combustion is considered to be one of the main causes of the release of large amounts of CO2 into the atmosphere, which causes the greenhouse effect and, consequently, global warming. Therefore, it is in Western Europe, where the idea of climate protection enjoys widespread public support, that various specific measures are being taken to accelerate the abandonment of coal. The greatest successes in the transformation of coal mining enterprises have been achieved in Germany, Great Britain, America, Canada, Poland and other European countries. In most countries, the transformation of coal mining enterprises takes place in two main directions: the creation of individual enterprises (business projects) or technology parks. Although the process of structural change and the abandonment of coal have their differences in each country and region, Germany's experience has identified major challenges and shaped strategic options for structural change in mining regions. The considered ecological and economic projects in other countries have an opportunity to be realized also at our coal mining enterprises.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".