Assisted Gene Flow to Facilitate Local Adaptation to Climate Change
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
Assisted gene flow (AGF) between populations has the potential to mitigate maladaptation due to climate change. However, AGF may cause outbreeding depression (especially if source and recipient populations have been long isolated) and may disrupt local adaptation to nonclimatic factors. Selection should eliminate extrinsic outbreeding depression due to adaptive differences in large populations, and simulations suggest that, within a few generations, evolution should resolve mild intrinsic outbreeding depression due to epistasis. To weigh the risks of AGF against those of maladaptation due to climate change, we need to know the species' extent of local adaptation to climate and other environmental factors, as well as its pattern of gene flow. AGF should be a powerful tool for managing foundation and resource-producing species with large populations and broad ranges that show signs of historical adaptation to local climatic conditions.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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