Water temperature regimes affect cataract development in smolting Atlantic salmon, <i>Salmo salar</i> L.
Bibliographic record
Abstract
The effect of fluctuation in water temperature during the freshwater rearing stage on cataract development in Atlantic salmon was investigated. Presmolts were exposed to either constant low or high temperatures, or high temperature with cold‐water fluctuations. Temperature fluctuations caused the fastest growth rate and most severe cataracts, whilst low temperature resulted in slow growth and minor cataracts. The fish were then individually marked and maintained together in a seawater net pen for 7 months. Cataract progressed in all groups, fish from the low temperature group showed the fastest subsequent growth, and developed cataracts to the same degree as fish from the other groups. There were no statistically significant differences in selenium‐dependent glutathione peroxidase levels in pooled samples of aqueous humour, lens and plasma from fish with or without cataract, except from the last freshwater samples, where cataractous fish showed lower plasma activity. Plasma cortisol and glucose levels did not differ, nor did whole eye riboflavin levels. Higher concentrations of some free amino acids in whole eyes were found in fish with cataract. This study shows that fluctuation in water temperature may cause both increased growth rate and cataract development in Atlantic salmon and that cataract development initiated in the freshwater rearing phase continues after transfers to seawater.
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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".