Acoustic telemetry predation sensors reveal the tribulations of Atlantic salmon (<i>Salmo salar</i>) smolts migrating through lakes
Bibliographic record
Abstract
Abstract Smolt migration through lakes is hazardous, as the predation pressure can be extreme and the hydrology a great contrast to that of a riverine area. However, the mechanisms yielding these challenges have been scarcely investigated. We conducted an acoustic telemetry field study in Lake Evangervatnet, Voss, Norway, utilising Vemco V5 predation tags. Atlantic salmon (Salmo salar) smolts (N = 20) were tagged with the novel predation sensor tag to investigate mortality, the lacustrine migration behaviour of smolts, and the applicability of these tags for smolt studies. A total of 60% of tagged Atlantic salmon (Salmo salar) smolts perished in the lake. Half of the mortalities (30% of tagged fish) were directly attributed to predation by brown trout (Salmo trutta) based on predation sensors. The surviving smolts were slow to traverse the 6.5 km lake, with progression rate between lake inlet and outlet on average 0.016 m/s over a mean of 7.9 ± 6.2 (SD) days. Acoustic detections revealed a consistent pattern of nocturnal migration and multidirectional movements within the lake. By running a series of correlated random walks under varying parameters and comparing the simulated travel times to the observed travel time used by the tagged smolts, we emulated the observed behaviour of the smolts. These simulations suggested that smolts lacked the ability to efficiently navigate through the lake, instead swimming in random directions until they reached the lake outlet. Predation sensors can offer improved resolution when tracking the behaviour and fate of smolts and can facilitate better mitigation efforts by identifying survival bottlenecks and separating predation from non‐predatory mortality.
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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".