Coho Salmon (Oncorhynchus kisutch) Prefer and Are Less Aggressive in Darker Environments
Bibliographic record
Abstract
Fish are capable of excellent vision and can be profoundly influenced by the visual properties of their environment. Ambient colours have been found to affect growth, survival, aggression and reproduction, but the effect of background darkness (i.e., the darkness vs. lightness of the background) on preference and aggression has not been evaluated systematically. One-hundred Coho salmon (Oncorhynchus kisutch), a species that is increasing in popularity in aquaculture, were randomly assigned to 10 tanks. Using a Latin-square design, every tank was bisected to allow fish in each tank to choose between all the following colour choices (8 choices in total): black vs. white, light grey, dark grey, and a mixed dark grey/black pattern, as well as industry-standard blue vs. white, light grey, dark grey, and black. Fish showed a strong preference for black backgrounds over all other options (p < 0.01). Across tests, preference strength increased with background darkness (p < 0.0001). Moreover, having darker backgrounds in the environment resulted in less aggressive behaviour throughout the tank (p < 0.0001). These results provide the first evidence that darker tanks are preferred by and decrease aggression in salmonids, which points to the welfare benefits of housing farmed salmon in enclosures containing dark backgrounds.
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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) | 0.002 | 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".