Priorities for embedding ecological integrity in climate adaptation policy and practice
Bibliographic record
Abstract
Humanity must adapt rapidly to climate change as the impacts accelerate. Growing scientific evidence underscores the role of ecological integrity in improving adaptation outcomes for nature and people by providing climate refugia for biodiversity, buffering natural hazards, protecting freshwater resources, and benefiting human health. However, climate adaptation initiatives have largely neglected to prioritize ecological integrity, even though it is critical for effective adaptation and achieving global conservation goals. Here, we highlight how climate and biodiversity policy and practice can help manage ecosystems for ecological integrity and ecological and social adaptation outcomes. We discuss challenges associated with operationalizing ecological integrity in adaptation policy and practice and describe seven priorities for scientists, policymakers, and practitioners to improve adaptation outcomes through supporting the retention of high-integrity ecosystems and the restoration of low-integrity ecosystems. Finally, we show how linking these priorities to UN frameworks on climate, biodiversity, and sustainable development would help attain the best outcomes for people and nature in a changing climate.
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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 | 0.194 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.015 | 0.042 |
| Scholarly communication | 0.034 | 0.037 |
| Open science | 0.008 | 0.035 |
| Research integrity | 0.028 | 0.049 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".