Hormonal manipulations of growth rate and its influence on predator avoidance - foraging trade-offs
Bibliographic record
Abstract
Theoretical investigations into the impact that predators exert on prey species suggest that two parameters, growth rate and mortality rate, should be the most influential in determining when animals should risk exposure to a predator in order to achieve higher feeding rates. While these two parameters have usually been assumed to be environmentally determined, we used thyroid hormone (3,3prime,5-triiodo-L-thyronine (T3)) to manipulate growth rates and examine the behavioural consequences associated with these manipulations. In two experiments, we examined how the growth rate of fathead minnows (Pimephales promelas) is affected by treatment with T3, and used the results from this experiment to make a priori predictions about their relative willingness to risk exposure to a predator in order to receive increased feeding rates. The first experiment demonstrated that T3 significantly reduced the growth rates of fathead minnows compared with an unmanipulated control. When groups were compared in their relative willingness to risk exposure to a predator, manipulated growth rates in the first experiment were an accurate predictor of behaviour; groups with relatively high growth rates were more willing to risk exposure to a predator. These results are consistent with the theoretical expectation that growth rates should be an important factor determining decisions that involve trade-offs.
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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 | 0.001 |
| 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) | 0.001 | 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".