Analysis of territorial waste management schemes of the most populated regions of the Russian Federation
Bibliographic record
Abstract
Within the framework of the proposed article, the territorial waste management schemes of the seven most populated subjects of the Russian Federation are analyzed. The success of the reform of the national solid waste management industry is impossible without an adequate regional interpretation of the goals specified in the Industry Development Strategy and the corresponding Federal Project. The key indicators reflected in these documents are an increase in the share of municipal solid waste utilization, up to 36 %, for sorting — up to 60 % by 2024. The necessity to reduce the share of imported equipment for municipal solid waste treatment to 22 % is also highlighted. The authors of the article analyzed the territorial schemes of Moscow, the Moscow Oblast, the Krasnodar Krai, St. Petersburg, the Sverdlovsk Oblast, the Rostov Oblast, and the Republic of Bashkortostan. As a result of the study, it was revealed that in some of the analyzed regions of the Russian Federation an imbalance in the target indicators set in the territorial schemes takes place. In addition, the following disadvantages of the analyzed territorial schemes were highlighted: lack of a description of technological solutions which are planned to be used to achieve the key indicators, plans of commissioning of insufficiently high-tech facilities and construction of new landfills, almost complete lack of information on import substitution. The authors also noted the difficulties of gaining access to files with territorial schemes on the Internet, since there are no unified standards for their publication in Russia. This fact reduces the information transparency of the industry for both population and the expert community.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".