Essential Biodiversity Variables and Essential Ecosystem Services Variables for the Implementation of Biodiversity Conservation and Sustainable Development Goals
Bibliographic record
Abstract
As nations design a framework and a process for implementing the new goals and targets set by the Convention on Biological Diversity (CBD), the question on how to report on the successes and failures of policy implementation is becoming more salient. In this paper, we demonstrate the potential role of Essential Biodiversity Variables (EBVs), Essential Ecosystem Services Variables (EESV) and their derived indicators in monitoring, planning, and implementing multiscale policy frameworks across spatial scales. We first introduce the EBVs and EESVs and then analyze their role in the UN CBD Global Biodiversity Framework, the Systems of Environmental Economic Accounting, Sustainable Development Goals, and the Intergovernmental Platform on Biodiversity and Ecosystem Services. We illustrate how the EBVs and EESVs can be used across scales via application cases. We also discuss the use of EBVs and EESVs in scenarios and modelling for strategic policy planning. This paper presents the values of the Essential Variables in implementing biodiversity conservation and sustainable development goals, optimizing the integrative use of scientific, policy, and data frameworks and scalable and repeatable workflows from primary data to indicators.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".