The use of predator tags to explain reversal movement patterns in Atlantic salmon smolts (<scp><i>Salmo salar</i></scp> L.)
Bibliographic record
Abstract
Acoustic telemetry has seen a rapid increase in utility and sophistication in recent years and is now used extensively to assess the behavior and survival rates of many aquatic animals, including the Atlantic salmon. As part of the salmon's complex life cycle, salmon smolts are thought to make a unidirectional migration from fresh water to the sea, which is initiated by changes in their physiology. However, some tag movement patterns do not conform with this and can be difficult to explain, particularly if the tagged fish has been eaten by a predator. This study combines the use of predator tags with machine learning techniques to understand the fate of migrating salmon smolts and thereby improve estimates for migration success. Over 3 years between 2020 and 2022, 217 salmon smolts (including wild and hatchery-reared ranched fish) were acoustically tagged and released into an embayment on the west coast of Ireland. Some tagged smolts were observed to return from the estuary back into a saline lagoon through which they had already migrated. To distinguish between the movement of a salmon smolt and that of a predator, predator tags were deployed in migrating smolts in 2021 and 2022. The addition of a temperature sensor in 2022 enabled the determination of predator type causing the returning movement. A significant number of predator tags were triggered, and the patterns of movement associated with these triggered tags were then used with two types of machine learning algorithms (hierarchical cluster analysis and random forest) to identify and validate the behavior of smolts tagged without extra sensors. Both models produced the same outputs, grouping smolts tagged with predator tags with smolts tagged without the additional sensors but showing similar movements. A mammalian predator was identified as the cause of most reversal movement, and hatchery-reared ranched smolts were found to be more likely predated upon by this predator than wild smolts within the lake and the estuary. However, overall migration success estimates were similar for both wild and hatchery-reared ranched fish. This study highlights the value of predator tags as an essential tool in the overall validation of detection data.
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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 | 0.001 | 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) | 0.000 | 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".