The fate of intracoelomic acoustic transmitters in Atlantic salmon (Salmo salar) post-smolts and wider considerations for causal factors driving tag retention and mortality in fishes
Bibliographic record
Abstract
Abstract Acoustic telemetry is a widely used method in assessing behavioural dynamics in fishes. Telemetry transmitters (tags) are often surgically implanted in the coelom of the animal with limited in situ testing and sometimes only assuming that they have minimal rates of post-release tag shedding and mortality. However, fish are capable of expelling tags and mortalities do occur following release, with the mechanism (s) underlying these effects not well-understood. The purpose of this research was to address causal factors underlying tag expulsion and tagging mortality in fishes. We conducted an empirical assessment of tag retention and post-surgical mortality rates in post-smolt Atlantic salmon ( Salmo salar ) fitted with dummy (non-transmitting) acoustic tags over a 92-day monitoring period. This was complimented with a meta-analysis of factors affecting tag retention and post-surgical mortality rates in the wider literature. Post-smolt salmon had high rates of tag expulsion (54.8%), impaired growth, and a foreign body response evident but exhibited low rates of mortality following tag implantation (≤ 5.1%). The meta-analysis showed that mortality was generally low across all studies (12.4%) and was largely unaffected by model cofactors. Tag retention rates were high among the studies investigated here (86.7%) and had a weak negative relationship with tag:body mass ratios. Our results suggest that while mortality is often low among tagging studies, including this one, caution must be exercised in assessing stationary tag location data as they may represent an expelled tag rather than a mortality event. Our results also indicate that tag dimensions are not nearly as important as the tag:body mass ratio.
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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".