Bibliographic record
Abstract
Populations that experience severe stress may avoid extinction through adaptation by natural selection. This process is called evolutionary rescue and has been studied under different names in medicine, agriculture, and conservation biology. It is a component of the emerging field of eco-evolutionary dynamics, which investigates how the ecological attributes of species may evolve rapidly under strong selection. Its distinguishing feature is to combine the evolutionary concept of relative fitness with the ecological concept of absolute fitness in a synthetic theory of persistent adaptation. The likelihood of rescue will depend both on attributes of the population, particularly abundance and variation, and on properties of the environment, particularly the rate and severity of deterioration. Medical interventions (e.g., the administration of antibiotics), agricultural practices (e.g., the application of pesticides), and population ecology (e.g., the effects of species introductions) provide numerous examples of evolutionary rescue. The general theory of rescue has been tested in laboratory experiments with microbes, in which experimental evolution shows how different treatments affect the frequency of rescue. Overall, these experiments have supported the predictions of general theory: In particular, abundance, variation, and dispersal have pronounced and repeatable effects on the rescue of populations and communities. Extending these laboratory results to the field is a major task for future research.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".