Multi‐scale considerations for identifying and managing climate‐change refugia in grassland ecosystems
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
Abstract Grasslands are globally imperiled. Centuries of intensive agriculture, anthropogenic development, woody encroachment, and the disruption of historical disturbance regimes have degraded native grasslands with wide‐ranging impacts on grassland species. Exacerbating these threats, modern climate change is rapidly affecting these highly exposed systems. Grasslands are perhaps the most intensively managed ecosystem in North America; private landowners, governmental, and nonprofit agencies expend considerable resources creating and maintaining grasslands using prescribed burning, managed grazing, and mechanical restoration. Climate‐change refugia are critical for climate‐vulnerable species and can help buffer populations from acute and chronic climate‐related stressors. Given the widespread geography of grasslands and the intensive management they require, there is untapped potential to integrate multi‐scaled concepts of climate‐change refugia with current grassland conservation strategies. Using declining grassland birds as a case study, we explore how the concepts of climate‐change refugia, spanning from macro‐scale conservation planning to microclimate management, may aid in the conservation and management of climate‐vulnerable grassland species.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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