Influence of food abundance on individual behaviour strategy and growth rate in juvenile brown trout (<i>Salmo trutta</i>)
Bibliographic record
Abstract
We studied the benefit of being territorial as an effect of food abundance by measuring the proportions of individuals that displayed a territorial, floating (individuals occasionally displayed territorial behaviour), or nonterritorial (shoaling) behaviour strategy, and individual growth rates. Also, swimming activity was monitored as an indicator of emigration. Replicate groups of 12 juvenile brown trout (Salmo trutta) were released into an artificial stream channel, fed according to one of four food regimes for 10 days, and allocated to one of three behaviour categories. There was no significant relationship between food abundance and the number of territorial individuals. Instead, the proportions of individuals that displayed the alternative behaviour strategies, i.e., floating and nonterritorial, changed with food abundance. At the lowest food level, more individuals displayed nonterritorial than floating behaviour, but these two strategies were equally represented at the highest food level. The difference in growth rates with respect to behaviour category was highest at intermediate food levels. At the highest and lowest food levels, nonterritorial fish and floaters grew nearly as fast or as poorly, respectively, as the territorial individuals. Swimming activity between the two sections of the stream tank was greatest at the lowest food level, and there was a general tendency (not significant) for the floaters to exhibit the greatest activity.
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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".