Do juvenile Atlantic salmon (<i>Salmo salar</i>) use chemosensory cues to detect and avoid risky habitats in the wild?
Bibliographic record
Abstract
We examined whether juvenile Atlantic salmon ( Salmo salar ) in the wild adjust their behaviour in response to chemical cues of predator activity during a 4-week period after emergence from gravel nests. In each of seven 75 m2 sites in Catamaran Brook, New Brunswick, Canada, we established three contiguous sections differing in perceived predator activity by releasing stream water in control sections, conspecific alarm cues in risky sections, and nothing in buffer sections in both 2006 and 2007. As predicted, the density of young-of-the-year (YOY) salmon tended to decrease in alarm cue sections, while it increased in control and buffer sections. After the 2-week manipulation in 2006, we switched treatments so that buffer sections became alarm cue sections and alarm cue sections became buffer sections for an additional 2-week period. After the switch, the number of YOY increased least in the new alarm cue sections and most in control and new buffer sections. In contrast with YOY, the density of age 1+ parr was not affected by the experimental treatments. Our results suggest that YOY salmon can use chemical alarm cues to assess the predator activity of habitats in the wild.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".