In Situ Swimming Behavior of Lake Trout Observed Using Integrated Multibeam Acoustics and Biotelemetry
Bibliographic record
Abstract
Abstract Multibeam echo sounder systems allow the in situ observation of swimming and foraging behavior and give insights into the ecology of fish at the individual level. In Lake Opeongo, Ontario, 16 adult lake trout Salvelinus namaycush were surgically implanted with ultrasonic tags, released, and studied by means of mobile fisheries acoustics. The transmitted pulses from the ultrasonic tags could be detected and displayed within the multibeam echogram in real time. Tagged lake trout were relocated on 131 occasions over 12 d, for a total of 11.7 h of echogram observations. From these events we observed and quantified the spatial relationships of individual lake trout to other fish targets, schools of cisco Coregonus artedi, and the surrounding habitat. We found that all but one tagged lake trout spent at least a portion of their time close to the lake's bottom, but interestingly, many made rapid vertical swimming movements into the water column. These burst vertical movements were sometimes targeted at schools of cisco, such attacks always occurring from below the schools. During such interactions, the lake trout showed distinct peaks in swimming speed when they were between 2.4 and 6.4 m from the schools; we interpret this as the range of their reactive distance in the field. Some of the lake trout were also found to travel alongside of or to actively swim toward other fish targets, whereas others were more solitary. This type of information, made possible by the integration of fisheries acoustics and biotelemetry technology, gives us a fuller understanding of the ecology of aquatic predators and their prey and provides the direct measurements needed to quantify the bioenergetics of lake trout in their natural environment.
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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".