Evaluating the effect of dorsal muscle biopsies on adult Atlantic salmon growth and marine return rates
Bibliographic record
Abstract
Increasing conservation and animal-welfare concerns have driven the development of non-lethal sampling of fish populations, with the use of muscle tissue biopsies now being routinely applied as a sampling method in the wild. Crucial to the success of non-lethal sampling, however, is an evaluation of the short- and long-term consequences of the treatment and ultimately the determination of how these may affect organism mortality and other fitness-related traits. The current study evaluated the use of a dorsal muscle biopsies on post-spawned Atlantic salmon emigrating to sea and undertaking a 2-month long-feeding migration before returning to spawn. Using mark-recapture, return rates and growth were compared between fish that were biopsied and externally tagged, and a control group tagged only with external tags. The biopsy treatment showed no lasting effects on fish as estimated from the two key fitness-related parameters. Results, therefore, suggest the technique can be more widely applied to gather information on marine migrating Atlantic salmon and other anadromous fishes that can be intercepted as they descend and ascend rivers during seasonal migrations. Coupled with modern tagging technologies, the use of biopsies may facilitate an improved understanding of movement and its consequences in terms of feeding patterns and growth.
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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 | 0.001 |
| 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".