Priorities for embedding ecological integrity in climate adaptation policy and practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it