The Response of Lake Trout to Manual Tracking
Bibliographic record
Abstract
Abstract The use of telemetry is widespread in fisheries research, and manual tracking of fish is considered acceptable for data collection. However, it has never been shown whether the use of boats with powered engines, which is common in manual tracking, influences the distribution and behavior of fish. We examined whether general boat traffic or focused manual tracking activity altered movement patterns of adult lake trout Salvelinus namaycush in a small boreal lake at the Experimental Lakes Area of northwestern Ontario, Canada. In the summer and fall of 2002, we used automated fish positioning systems to compare the behavior of lake trout during 1‐h disturbance periods (boat traffic or manual tracking) with behavior in the preceding 1‐h baseline periods (no disturbance). In addition, we compared behavioral differences of lake trout during disturbance and baseline periods (experimental trials) with a similar period of time when no boats were on the lake (control trials). This comparison allowed us to determine whether the observed changes in behavior during normal boat traffic or manual tracking were within the natural range of variation. Overall, we observed no effect of boat traffic or manual tracking on the depth, speed, and path predictability of lake trout. Similarly, the changes in lake trout behavior between the disturbance and baseline periods of the experimental trials were all within the natural range determined by control trials. The response of lake trout to manual tracking was not related to their proximity to the motorboat, both when lake trout were in deep water (6 m; summer) and when they were in shallow water (2 m; fall spawning season). The lack of significance of the relationship between patterns of fish behavior and manual tracking activity provides support for the continued use of this method in fisheries research.
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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".