Effects of intracoelomic transmitter implantation on metabolic rate, swimming performance, growth and survival in juveniles of two salmonids
Bibliographic record
Abstract
Abstract In this study, we investigated the effects of acoustic tag implantation on standard and routine metabolic rate (SMR and RMR, estimated via oxygen consumption), critical swimming speed (Ucrit), survival and growth in juveniles of rainbow trout Oncorhynchus mykiss and lake trout Salvelinus namaycush. Tag burdens ranged from 1.8% to 7.5% across the two species. Growth rates in acoustic‐tagged fish were equal to or higher than those in other treatments. Acoustic‐tagged S. namaycush had a marginally lower Ucrit than controls but that effect was not replicated in the O. mykiss experiment. Tagging did not have clear effects on metabolic rate but there was an interaction whereby SMR and RMR tended to increase with time since surgery in tagged O. mykiss but not in other treatments (the same trend did not occur in S. namaycush). Survival was high across treatments (mean 98% survival among O. mykiss, 97.5% among S. namaycush). There were no statistically significant effects of tag burden (percentage of body mass) except for a weak negative relationship with growth rate (across species) and a weak positive relationship with Ucrit but only in the O. mykiss. Collectively, our findings suggest there were minor, context‐dependent effects of acoustic tagging in juvenile S. namaycush and O. mykiss during an eight‐week laboratory experiment. Further research will be required to assess whether tagging can cause meaningful behavioural effects in these species in captivity or in the wild and whether there is a tag burden threshold above which deleterious effects consistently occur.
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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".