Density-dependent effects of prior residence and behavioural strategy on growth of stocked brown trout (<i>Salmo trutta</i>)
Bibliographic record
Abstract
When animals face temporally periods of strong intraspecific competition, prior residency, available resources, and their competitive abilities in terms of size and behavioural strategy affect their chances of survival. Density, individual size, and behavioural strategy had the strongest effect on growth. The mean growth rate of both resident and intruding brown trout (Salmo trutta L., 1758) decreased with increasing density, and the largest individuals were the most successful ones independently of the density of prior residency. An aggressive behavioural factor was beneficial at the lowest and intermediate densities, whereas a nonaggressive behavioural factor was beneficial at higher densities. Prior residency had no overall significant effect on growth, as the effect was highly density-dependent. The difference in growth rate between introduced and resident individuals was significant only at high density and low food abundance per individual. The intruders had a significantly lower growth rate at high densities and fewer individuals had a high growth rate. These results suggest that stocking fish at densities exceeding the carrying capacity of the habitat results in fewer individuals that are able to compete for resources than if fewer individuals were stocked. Brood stock can then be used in a more efficient way.
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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".