Feeding on Profitable and Unprofitable Prey: Comparing Behaviour of Growth‐Enhanced Transgenic and Normal Coho Salmon (<i>Oncorhynchus kisutch</i>)
Bibliographic record
Abstract
Abstract We compared the performance of normal and growth hormone‐transgenic coho salmon feeding on surface drifting edible and inedible novel prey items in various social environments. With an inherently higher appetite, we predicted that transgenic fish would be more willing to feed on novel prey, and that visual company with another fish would enhance this difference further. Transgenic and normal fish, of similar size and age, were equally willing to attack both the edible (live insects) and inedible (artificial angling lure flies) prey, but transgenic fish did so faster and were more likely to make repeated attacks. Transgenic fish managed to seize and consume the edible prey after fewer attacks than did normal fish. However, swallowing of prey took longer than for normal fish. More transgenic individuals interacted with the inedible prey compared with normal salmon, and initially, transgenic fish in visual company with another fish also interacted more with the prey than single transgenic or any constellation of normal focal fish. With repeated exposures, the number of individuals attacking and the number of interactions with the prey decreased. These responses were stronger in transgenic fish, partly explained by the initially low response in normal fish. The observed differences are most likely the consequences of elevated levels of growth hormone in transgenic fish generating enhanced feeding motivation and reinforcement capacity. In a natural environment, the performance of a growth hormone‐transgenic fish may therefore depend on the relative abundance of profitable vs. unprofitable prey, as well as the presence of other transgenic individuals.
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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 | 0.000 |
| 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".