Building Ecological Resilience in Highly Modified Landscapes
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
Ecological resilience is a powerful heuristic for ecosystem management in the context of rapid environmental change. Significant efforts are underway to improve the resilience of biodiversity and ecological function to extreme events and directional change across all types of landscapes, from intact natural systems to highly modified landscapes such as cities and agricultural regions. However, identifying management strategies likely to promote ecological resilience remains a challenge. In this article, we present seven core dimensions to guide long-term and large-scale resilience planning in highly modified landscapes, with the objective of providing a structure and shared vocabulary for recognizing opportunities and actions likely to increase resilience across the whole landscape. We illustrate application of our approach to landscape-scale ecosystem management through case studies from two highly modified California landscapes, Silicon Valley and the Sacramento–San Joaquin Delta. We propose that resilience-based management is best implemented at large spatial scales and through collaborative, cross-sector partnerships.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it