Approaches to assess land degradation risk: a synthesis
Bibliographic record
Abstract
Land degradation adversely affects the well-being of approximately 3.2 billion people worldwide and results in a loss of about 10% of annual gross domestic product. Degradation dynamics are often creeping and non-linear, hence the adverse consequences are often not immediately perceived. In consequence, methods for assessing future risks of land degradation and risk reduction strategies are trailing far behind those that have been developed within disaster risk research for natural hazards that appear as shocks, such as floods or earthquakes. Therefore, the objective of this paper is to analyze existing land degradation risk assessment approaches to assess what is hindering a link to land management strategies. The synthesis presented here reveals that while approaches to calculate land degradation risk have evolved to capture ever more processes and factors involved in land degradation, no consistent conceptual framework for land degradation risk has been developed to the present day. Key identified short-comings are that risk terminology is not consistent across and within studies and that there is often no distinction between a degradation status assessment and an assessment of future risk. Damage is rarely explicitly considered or quantified, and in the majority of studies there is no clear distinction between processes and drivers, hazard and vulnerability. Finally, novel conceptual ideas integrating the risk framework developed within disaster risk research are proposed to stimulate debate and facilitate the development of effective risk reduction measures for land degradation.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".