Foraging activity and habitat use throughout an annual migration of adult walleye (Sander vitreus) from the Trent River in eastern Lake Ontario
Bibliographic record
Abstract
Mature walleye (Sander vitreus) that spawn in the Trent River conduct long-distance annual migrations into eastern Lake Ontario that begin and end in the Bay of Quinte. This scale of movement likely reflects seasonal spawning activity in the spring and a combination of temperature and foraging preferences at other times of the year. This study used a combination of acoustic transmitters and pop-off data storage tags to collect high-resolution data on temperature, depth, rate of vertical movement (ROVM), and rate of horizontal movement (ROHM) during these migrations. We tested the theory that post-spawn fish migrating to Lake Ontario experience colder water temperatures than those remaining in the upper Bay of Quinte, and offset this cost with greater foraging, as indicated by ROVM. We also documented the trends in these variables seasonally at the daily and hourly level. Temperature experienced by walleye in the lake (11.56 °C; SE ± 0.1) was on average 5.33 °C colder than in the upper bay (16.89 °C; SE ± 0.3), and there was a 15.5% increase in ROVM for fish in the lake. All the measured variables had significant seasonal trends, while only temperature, depth, and ROVM had significant hour of day trends. Sex based differences were limited to males having greater annual ROVM than females. There were differences in thermal habitat selection and vertical activity measures between the upper bay and Lake Ontario, which supported the current conceptual model of post-spawn walleye migration from the Bay of Quinte. Vertical activity peaked during crepuscular periods during the summer and fall when water temperatures promoted growth. This study demonstrates the value of combining tagging techniques to collect high-resolution data across multiple aspects of annual fish migrations.
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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 | 0.001 | 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".