Novel insights gained from tagging walleye (Sander vitreus) with pop-off data storage tags and acoustic transmitters in Lake Ontario
Bibliographic record
Abstract
Improvements in electronic tagging techniques provide new opportunities to gain insights into fish habitat selection and behaviours that have been difficult to capture using traditional assessment methods. However, data from acoustic telemetry studies in large freshwater systems may bias our understanding of fish habitat use and behaviour because of the typically low sampling frequency of transmitters as well as the limited spatial coverage and distribution of receivers in a waterbody. This study combined acoustic transmitters and pop-off data storage tags (pDSTs) on individual walleye in Lake Ontario to gain a better understanding of the feasibility and utility of double tagging a large nearshore freshwater fish. High frequency pDST data (every 2 s) revealed a novel diving behaviour by walleye which made repeated rapid dives beyond their standard daily depth range. Comparison of data from the two tag types showed that in this large freshwater system with low overall receiver coverage (∼4%), mean monthly and daily depth and temperature occupancy of walleye were similar, although many of the extreme values observed in the pDST data were not observed in the acoustic data. The accuracy of daily vertical distance travelled by walleye and summertime diving parameters was dependent on sampling frequency and only the pDST logging on a 2 s interval was able to provide reliable results. The results of this study show that novel insights can be gained, for fish large enough to handle the burden of multiples tags, ranging from spatial ecology to diving behaviours.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".