Science for improving the monitoring and assessment of dryland degradation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it