The adaptive significance of population differentiation in offspring size of the least killifish, <i>Heterandria formosa</i>
Bibliographic record
Abstract
We tested the hypothesis that density-dependent competition influences the evolution of offspring size. We studied two populations of the least killifish (Heterandria formosa) that differ dramatically in population density; these populations are genetically differentiated for offspring size, and females from both populations produce larger offspring when they experience higher social densities. To look at the influences of population of origin and relative body size on competitive ability, we held females from the high-density population at two different densities to create large and small offspring with the same genetic background. We measured the competitive ability of those offspring in mesocosms that contained either pure or mixed population treatments at either high or low density. High density increased competition, which was most evident in greatly reduced individual growth rates. Larger offspring from the high-density population significantly delayed the onset of maturity of fish from the low-density population. From our results, we infer that competitive conditions in nature have contributed to the evolution of genetically based interpopulation differences in offspring size as well as plasticity in offspring size in response to conspecific density.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".