What Do We Really Know About Adaptation at Range Edges?
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Not applicableConsensus signal: Not applicable
- Genre
- Candidate signal: ReviewConsensus signal: none
- Teacher disagreement score
- 0.507
- Threshold uncertainty score
- 0.422
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
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)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.243 · how far apart the two teachers sit on this one work
- Validation status
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
Abstract
Recent theory and empirical evidence have provided new insights regarding how evolutionary forces interact to shape adaptation at stable and transient range margins. Predictions regarding trait divergence at leading edges are frequently supported. However, declines in fitness at and beyond edges show that trait divergence has sometimes been insufficient to maintain high fitness, so identifying constraints to adaptation at range edges remains a key challenge. Indirect evidence suggests that range expansion may be limited by adaptive genetic variation, but direct estimates of genetic constraints at and beyond range edges are still scarce. Sequence data suggest increased genetic load in edge populations in several systems, but its causes and fitness consequences are usually poorly understood. The balance between maladaptive and positive effects of gene flow on fitness at range edges deserves further study. It is becoming increasingly clear that characterizations about degree of adaptation based solely on geographical peripherality are unsupported.
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.
The record
- Venue
- Annual Review of Ecology Evolution and Systematics
- Topic
- Genetic diversity and population structure
- Field
- Biochemistry, Genetics and Molecular Biology
- Canadian institutions
- University of British Columbia
- Funders
- not available
- Keywords
- Adaptation (eye)TraitRange (aeronautics)Divergence (linguistics)Local adaptationEvolutionary biologyGenetic FitnessBiologyComputer scienceSelection (genetic algorithm)Artificial intelligenceSociologyPopulationDemography
- Has abstract in OpenAlex
- yes