Assessing Acoustic Tagging Effects on Survival, Growth, and Swimming Ability of Juvenile Lake Sturgeon
Bibliographic record
Abstract
Abstract Acoustic telemetry is used to quantify fish movement, ecology, and habitat use and can contribute to assessing the success of species supplementation. In this field, a better understanding of the effects of tag burden (or the impact of an acoustic telemetry tag, which is related to the ratio of tag weight to body weight) is critical to ensure postrelease monitoring. Research on the effects of acoustic tag burden on imperiled fishes at different ontogenic stages, such as juvenile Lake Sturgeon Acipenser fulvescens, is limited. Our study provides key information for the selection of the largest acoustic tag with the greatest battery life possible (taking into account tag burden) to monitor the release success and movements of juvenile Lake Sturgeon stocked for reintroduction. We characterized tag burden effects by examining survival, TL, weight, Fulton's condition factor, and swim performance of individuals. We examined four groups of fish: control (anesthetized and no acoustic tag inserted; n = 24), sham control (anesthetized with incision sutured but no acoustic tag inserted; n = 24), Vemco V8 acoustic tag (2.0 g in air; n = 24), and Vemco V9 tag (4.4 g in air; n = 24). Acoustic tags were inserted into anesthetized fish, and the incision was sutured; tag burden (mean ± SE) ranged from 2.2 ± 0.06% to 4.6 ± 0.10% of total body weight. Results showed that the two tag burden treatments had no significant effects on growth or survival (compared to both control groups) across a 114-d study period and that critical swim speed at 12–20 d postsurgery was not significantly impacted by increasing tag burden. Because neither of the acoustic tag sizes had significant deleterious effects on the metrics studied, we recommend using a larger V9 tag (i.e., the most powerful tag with the longest battery life) for postrelease monitoring of reintroduced juvenile Lake Sturgeon.
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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".