Historical retrospective of environmental problems of oil-extracting regions (On the example of the Almetyevsk district of Tatarstan Republic in Russia in the second half of the XX century)
Bibliographic record
Abstract
© 2015, Canadian Center of Science and Education. All rights reserved. Article is directed on identification of resistant interrelation of industrial development of the concrete territory and ecological consequences of this development. On examples of archival materials are disclosed sources of formation of negative impact on environment of Tatarstan Republic. As the leading approach to research of a problem is chosen the method of the retrospective analysis which allows to track extent of industrial impact on environment of the Almetyevsk oil area throughout the semicentennial period. Prerequisites of need of formation of environmental policy for the region come to light. As a result of research are made conclusions that development of productive forces in the region the long time was carried out without its ecological features. The serious aggravation of an ecological situation became a result of it. However it brought also to that, production during the planning and implementation of the activity began to rely on the principle of greening. Materials of this article can be useful to theoretical and practical justification of expediency of implementation of economic activity with a support on the principles nature and resource saving.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 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".