Science for improving the monitoring and assessment of dryland degradation
Bibliographic record
Abstract
Abstract The United Nations Convention to Combat Desertification (UNCCD) commissioned its First Scientific Conference in 2009 to deliberate on ways to improve the global monitoring and assessment of dryland degradation to support decision‐making in land and water management. The papers included in this issue of Land Degradation & Development elaborate the reasoning behind the 11 recommendations that emerged from the Conference and were formally submitted to the UNCCD. These papers argue for a more holistic, harmonised and integrated approach to dryland monitoring and assessment, and describe scientific and institutional approaches for achieving this goal. A central challenge is to integrate human/social with environmental observations in accordance with the Convention's view that the interactions and tradeoffs between human development needs and land condition must be considered. A global monitoring and assessment regime should be established to gather and analyse relevant data on a routine basis, allowing locally‐relevant indicators to be aggregated into meaningful classes appropriate to different decision‐making levels. The underlying forces that cause changes in land condition should also be monitored and assessed so that remedial actions can target the true causes of dryland degradation, including social, economic, policy, institutional and knowledge drivers that have often been overlooked in the past. Monitoring and assessment should hybridise differing types of knowledge generated by different stakeholders in order to strengthen collective capacities to combat dryland degradation. An independent scientific advisory mechanism should be created to advise the UNCCD about the results emerging from the monitoring and assessment regime in order to improve decision‐making. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.031 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".