Biological carryover effects: linking common concepts and mechanisms in ecology and evolution
Bibliographic record
Abstract
The term ‘carryover effect' originally arose from repeated measures clinical experiments. However, the term has more recently been applied to ecological and evolutionary studies, often in migratory systems, which has led to an emphasis on non‐lethal effects across seasons. In this article, we suggest that ecological carryover effects can also occur between life‐history stages, developmental stages, physiological states, or social situations, and each will be associated with a discrete time‐scale. Therefore, we propose the working definition: In an ecological context, carryover effects occur in any situation in which an individual's previous history and experience explains their current performance in a given situation . This concept of carryover effects provides an explicit but highly flexible context for examining the mechanisms that drive non‐lethal interactions between distinct periods of an organism's lifetime, and unites the currently disparate fields investigating these effects in ecological systems. Greater communication among research fields and identifying mechanisms of carryover effects at different time scales will ultimately lead to a better understanding of the factors influencing variation in individual fitness.
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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 | 0.025 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.025 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".