An operational methodology to identify Critical Ecosystem Areas to help nations achieve the Kunming-Montreal Global Biodiversity Framework
Bibliographic record
Abstract
Abstract The Kunming-Montreal Global Biodiversity Framework (GBF) will become the most important multilateral agreement to guide biodiversity conservation actions globally over the coming decades. An ecosystem goal and various targets for maintaining integrity, restoring degraded ecosystems, and achieving representation in conservation areas feature throughout the GBF. Here, we propose an operational framework that combines disparate information on ecosystem type, extent, integrity, levels of protection, and risk of collapse to support the identification of irreplaceable ‘Critical Ecosystem Areas’ (CEAs), to help advance these ecosystem targets. The framework classifies each component ecosystem based on its integrity, importance in ensuring no ecosystem collapse and its relative value to achieving representation if protected. These CEAs are immediate conservation opportunities, given that they achieve multiple ecosystem goals and targets in the GBF. We showcase its application using Myanmar’s forested ecosystems as a case study and argue that it could be immediately used across all terrestrial ecosystems.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".