Feasibility of using surgical implantation methods for acoustically tagging alewife (Alosa pseudoharengus) with V5 acoustic transmitters
Bibliographic record
Abstract
Anadromous alewives (Alosa pseudoharengus) are abundant in the Canadian Maritimes, where they support lucrative commercial fisheries. Little is known about their coastal movement, and their potential to interact with anthropogenic structures. Acoustic telemetry can provide detailed information on the spatiotemporal distribution and survival of fishes in coastal areas, using information transmitted from tagged fishes and recorded by moored receivers. However, few acoustic telemetry studies have been performed on clupeids as they are extremely sensitive to handling, and are often compromised by surgical tag implantation. This research assesses the feasibility of a surgical tagging protocol using novel High Residency acoustic tags in alewives, and establishes a baseline of short-term tagging effects. Alewives from the Gaspereau River population were tagged between 2018 (n = 29) and 2019 (n = 96) with non-transmitting models of Vemco/Innovasea V5 HR tags. Tagging effects were evaluated based on recovery rate, reflex impairment, and necropsy-based health assessments. Alewives responded well to tagging, with low mortality (3%) and no observed instances of tag shedding 72 hours post-surgery. The use of sutures to close the incision site had no effect on recovery times. Water temperature and spawning condition had the greatest effect on the behavioural response of fish to tagging. Our findings suggest that, with proper handling and smaller acoustic tags, telemetry studies on alewives are feasible.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".