FOR THE CALCULATIONS OF ANTHROPOGENIC EMISSIONS FROM SOURCES AND REMOVALS BY SINKS OF GREEN HOUSE GASES IN FORESTRY AND AGRICULTURE FOR THE ANNUAL NATIONAL REPORT OF THE REPUBLIC OF KAZAKHSTAN
Bibliographic record
Abstract
The results presented in the article were obtained in the process of preparing the National Report on the inventory of anthropogenic emissions from sources and absorption of greenhouse gases not regulated by the Montreal Protocol, in terms of land use. The report is submitted annually by the Republic of Kazakhstan, as a party to the United Nations Framework Convention on Climate Change (UNFCCC). Preliminary calculations of the dynamics of carbon dioxide (CO2) associated with land use in the Republic of Kazakhstan for 1991...2020 showed that its absorption from the atmosphere by natural ecosystems could range from 3 to 50...60 million tons per year, and emissions into the atmosphere – from 10 to 45 million tons/year. In the process of research, a detailed analysis of the country's existing system of ground-based monitoring of land use and land resources was carried out with calculations of anthropogenic CO2 flows at various territorial levels. The results obtained confirm the possibility of calculating greenhouse gases at the zonal and regional levels. This confirms the possibility of additional control of calculations of greenhouse gas fluxes associated with land use and assessment of the potential for a possible increase in absorption volumes for natural ecosystems (Forests and Pastures) and reductions in emissions for the Croplands agroecosystem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".