Experience in application and adaptation of the land degradation neutrality concept in the Russian Federation
Bibliographic record
Abstract
Abstract The paper provides an overview of the—evolving methodology for the land degradation neutrality (LDN) assessment based on studies at national, regional, and local levels in Russia. A review of more than one hundred publications in Russian language over the past 6–7 years allowed for analysis of the following areas: LDN terminology, LDN assessment at different levels, adapting a transition matrix; using the LDN concept for economic valuation of land, estimating LDN baseline, and using LDN as an integral indicator for sustainable land management. With the LDN concept, a global approach to monitoring land degradation has become applicable beyond the limited geographic scope of the drylands. The paper observes how the LDN concept has been broadened with the introduction of the LDN Index proposed to evaluate the rate of LDN achievement; a proposal on reconstructing transition matrices and adding land cover sub‐categories; approach of integrating traditional national sectoral systems for assessing land quality with an LDN add‐on. The broader relevance of the paper includes the justification that it provides for using the LDN concept by policy‐makers at national and subnational levels, in particular in Russian‐speaking countries. It includes the application of additional indicators to capture soil erosion, salinity, soil depletion, aridity, etc., and using different site‐specific LDN baselines, not only those time‐based but also factoring natural background trends like climate change, natural succession cycles linked with geological and geomorphological processes. Approaches for LDN‐based economic valuation of lands and typology of sustainable land management practices and models were also fruitful.
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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 | 0.021 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".