The effects of abiotic factors and intraspecific versus interspecific competition on the diel activity patterns of Atlantic salmon (Salmo salar) fry
Bibliographic record
Abstract
We conducted semi-natural experiments to measure the relative contribution of various environmental factors and intraspecific and interspecific competition with an exotic invader on the daytime and crepuscular activity levels of Atlantic salmon ( Salmo salar ) fry. We demonstrated that interspecific competition with the exotic rainbow trout ( Oncorhynchus mykiss ) significantly increases the daytime activity of Atlantic salmon. The effect of intraspecific competition on the daytime activity of salmon was half that of interspecific competition. This indicates that the effect of rainbow trout was a combination of increasing density and the identity of the competitor. We also demonstrated that the effect of rainbow trout was probably the result of territorial interference between species. Moreover, we showed that water temperature simultaneously played an important role in explaining daytime activity of Atlantic salmon. During twilight, we observed no effect of competition on salmon activity, but environmental cues other than temperature (e.g., invertebrate drift, cloud cover) became significant predictors of activity. Feeding and growth rates of Atlantic salmon were not affected by the different levels of competition. Nevertheless, the exotic species may have a major impact by exposing the native species to increased risks of daytime predation.
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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".