Factors affecting short-term post-release survival probability of Lake Trout implanted with acoustic telemetry transmitters
Bibliographic record
Abstract
The use of acoustic telemetry is steadily expanding to help answer questions related to habitat use, movement, and behavior of fishes. Significant time and resources are invested to start acoustic telemetry studies; therefore, careful planning is needed to limit post-release mortality of tagged individuals. Deep, cold-water species present additional challenges to acoustic tagging because of changes in temperature and pressure experienced during capture. The objective of our study was to determine if capture method, surface water temperature, water depth, or fish size influenced short-term post-release survival of a deep, cold-water species, Lake Trout Salvelinus namaycush . In 2023, 299 Lake Trout were captured with angling or gillnets across Lake Ontario (Laurentian Great Lake – U.S. & CAN) and surgically implanted with acoustic transmitters. We estimated 30-day post-release mortality and 24-h post-release distance traveled for tagged Lake Trout. We used Cox proportional hazards models to identify factors affecting survival probability and multiple linear regression to identify factors affecting post-release distance traveled. Thirty-day post-release mortality was minimal (9.03 %, 27/299 Lake Trout); however, mortality was 6.37 times more likely for Lake Trout captured in gillnets compare to angling ( p = 0.003). Lake Trout length had a marginally significant effect on mortality ( p = 0.052) but capture depth and temperature did not ( p > 0.05). Lake Trout post-release distance traveled was not significantly influenced by capture gear, depth, temperature, or Lake Trout length ( p = 0.61). Our results indicate that tagging-induced post-release mortality is minimal for Lake Trout tagged in the spring, but survival can be increased by avoiding use of gillnets.
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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".