Analysis of ozone-depleting substances reporting in Russian regions for 2018-2024
Bibliographic record
Abstract
Depletion of the ozone layer of the atmosphere is one of the global environmental problems, to address which the Montreal Protocol on Substances that Deplete the Ozone Layer was adopted in 1987. The Montreal Protocol calls for a phase-out of the use of substances that deplete the ozone layer. To implement the requirements of the Montreal Protocol, state registration of the circulation of ozone-depleting substances has been organized on the territory of the Russian Federation. The procedure for state accounting of the circulation of ozone-depleting substances and the forms of annual reporting are established by the Russian Government. In this study, for the period 2018-2022 analyzed annual reports on the management of ozone-depleting substances provided by legal entities and individual entrepreneurs to the Russian Ministry of Natural Resources and assessed the dynamics of reporting provided by the Russian regions. It was revealed that the share of the Russian regions, organizations from which do not submit reports on the management of ozone-depleting substances to the Russian Ministry of Natural Resources is on average 36%, despite the fact that the management of ozone-depleting substances in these regions existed. It was established that over the course of five years, more than 50 organizations submitted reports on the management of ozone-depleting substances in just a few the Russian regions. Basically, organizations in the Russian regions either do not report at all, or report, but in small numbers. Based on the results of the work, we can conclude that currently the reporting of legal entities and individual entrepreneurs on the handling of ozone-depleting substances is of a formal nature and does not reflect the real picture of the volume of circulation of ozone-depleting substances in Russia, and therefore state accounting of the circulation of ozone-depleting substances requires improvement.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".