Effects of surgical implantation of electronic tags in fishes: a review and meta-analysis
Bibliographic record
Abstract
Abstract Electronic tags have been used for decades and continue to gain popularity. Tags are often implanted in the coelom of fishes, with a substantial body of experimental evidence now assessing the effects of those tags. We revisit a review of tagging effects (Cooke et al. 2011) to provide the most comprehensive review and meta-analysis of intracoelomic tagging effects in fishes to date. We reviewed 295 studies, yielding 226 laboratory trials reporting survival and 215 reporting tag retention. Mean survival in tagged fishes was 88.26% and tag retention was 87.05%. Both metrics were negatively related to tag:body mass ratios (i.e., tag burden), remaining above 90% on average with tag burdens below ~ 3–4%. Substantial heterogeneity existed among studies, with a modest portion of the variability in survival and tag retention explained by family, tag burden or trial duration. Across the tagging effects literature, 42% of papers documented negative effects of tagging on growth, and 34% found reduced critical swimming speeds relative to controls. Progress has been made in addressing some knowledge gaps, but biases in the tagging effects literature remain and only a small fraction of species used in electronic tagging studies are represented. Interest in understanding sublethal endpoints is growing, however, the importance of factors such as sex, reproductive status, sterility, and tag coatings remain poorly understood. Tag burdens of ~ 3–4% appear to balance tag size and unwanted tagging effects. More research is needed to better understand sublethal tagging effects and improve the inferences made from electronic tagging studies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 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".