Comparison of different land degradation indicators: Do the world regions really matter?
Bibliographic record
Abstract
Abstract In 2010, the Convention on Biological Diversity created the Aichi Biodiversity targets to aid the restoration of degraded ecosystems, which include the restoration of at least 15% of degraded ecosystems by 2020. A crucial step to achieve this goal is the development of nonbiased prioritization methodologies that help establish key areas for restoration. However, prioritization methodologies depend heavily on each country's economic capability, governance, internal politics, degradation level, and access to data. Because only 78 countries are considered high‐income economies, only this select group of countries would potentially have the necessary resources to compile the information needed to carry out a prioritization process. In this work, our aim was to analyze and compare key land degradation indicators (e.g., land use/change, primary productivity, biodiversity loss, soil organic carbon, degradation level, and social acceptance) in five world regions, with different incomes and political and cultural background, Africa, Asia, Europe, Latin America, North America (USA–Canada), and Oceania. We also grouped these key land degradation indicators by type (ecological, social, cultural, economic, and policy). Our results indicate that the different world regions seem not to have a direct impact on the number of land degradation indicators used. However, we found differences in the type of indicators used per region, partially denoting the idiosyncrasy of each of these regions. Our study shows that governance is important in the use of indicators although we suspect that there are other variables that could be at play not included in this study.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".