Bibliographic record
Abstract
Abstract A growing body of empirical work supports and informs the role of genetic variation and contemporary evolution in shaping ecological dynamics at the population, community and ecosystem levels. Although much progress has been made, I contend that reliance on several common empirical and inferential approaches is limiting forward progress in key areas, which leads me to several suggestions. More studies should focus on revealing eco‐evolutionary dynamics as they play out in the “real world,” as opposed to laboratories and mesocosms. At the community and ecosystem levels, increasing effort should be directed towards the importance of evolution acting through population density, as opposed to only direct per‐capita effects. More work should be directed towards the effects of whole‐community evolution, as opposed to the evolution of only particular focal species. New and innovative approaches are needed for studying how natural selection resists evolutionary and ecological change, thus generating cryptic eco‐evolutionary dynamics. Although simultaneous improvement on all of these fronts is perhaps impossible for any single research programme, even advances in one or more areas could dramatically improve our understanding of the prevalence, power and relevance of eco‐evolutionary dynamics. A plain language summary is available for this article.
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 | 0.026 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.037 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".