Bibliographic record
Abstract
The ELD Initiative is a global awareness study focused on raising political and public awareness of the impacts of land degradation and importance of land-based ecosystems, from an economic perspective. Initiated in 2010, the ELD Secretariat is based in Bonn, Germany, while the ELD Scientific Coordinators are based at the United Nations University – Institute of Water, Environment, and Health (UNU-INWEH) in Hamilton, Canada. The aims and objectives of the initiative are to: Prepare and present a cost-benefit analysis in the context of land degradation, in order to enable decision-makers in politics and business to take the necessary measures for better rural development and food security and promote sustainable land management (SLM). Systematise scientific studies of the economics of land degradation and reach agreements on scientific and methodological standards. Provide reliable data and application-oriented tools that are urgently needed as a basis for policy-making and investment decisions. In addition, data and practical examples will provide important material for media and public awareness work. Our Vision is to transform global understanding of the value of land and to create awareness of the economic case for both market and non-market values in sustainable land management to prevent the loss of natural capital, preserve ecosystem services, combat climate change, and address food, energy, and water security. The ELD Initiative is open to partnership and contributions from governments, foundations, development organizations, the private sector, and multilateral/bilateral donors. The initiative also actively encourages the participation of NGOs, farmers, agricultural associations, universities, research institutions, and international and local businesses in its endeavours. Please see
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.052 | 0.020 |
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".