Reconciling Different Forms of Ecological Integrity to Aid the Kunming‐Montreal Global Biodiversity Framework
Bibliographic record
Abstract
ABSTRACT With the Kunming‐Montreal Global Biodiversity Framework (GBF), the international community has committed to retaining ecosystems of high ecological integrity. Monitoring progress toward this target requires the identification of suitable indicators, but these are not universally recognized. In this study, we analyze available global maps of terrestrial ecological integrity and evaluate their representation of different dimensions of integrity (structure, composition, and function). Although 73% of terrestrial surface holds conservation value according to at least one map, less than 1% of land attains high integrity according to all of them. Solely relying on one indicator map risks overlooking the integrity value of at least 41 million km 2 of land, with some key areas for biodiversity conservation inadequately represented by these indicators of integrity. However, when used in combination, complementary dimensions of integrity help identify an area covering 41.1% of the terrestrial surface, two‐thirds requiring urgent conservation action. The synergistic use of existing measures offers considerable potential to guide the implementation of Target 1 of the GBF while supporting more equitable conservation paradigms. Developing robust indicators and understanding the link among different ecological dimensions is essential to protect ecosystems of high ecological integrity in the long term.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".