Seasonal habitat-use differences among Lake Erie’s walleye stocks
Bibliographic record
Abstract
Understanding the spatial ecology and habitat-use of Lake Erie’s commercially important walleye (Sander vitreus) population is imperative due to their large-scale seasonal migrations (>400 km) exposing them to five different jurisdictions in the USA and Canada. The objective of this study was to determine the habitat selected by walleye throughout the year and across Lake Erie. Here, we used acoustic telemetry to estimate walleye occurrence at three lake depth categories that were pertinent to biology (e.g., spawning) and management (e.g., quota allocation). Detection data from 851 adults during five continuous years identified consistent seasonal fluctuations in habitat selection across western (WB) and eastern (EB) basin walleye stocks. Sex-specific differences were also found during spawning periods (March-May) when males showed a stronger affinity to shallow waters <6 m than females. Also, EB stocks selected these shallow waters longer than WB stocks, likely due to differences in thermal patterns between basins. Deep water (>13 m) was readily selected between spring and winter (>6 months/year) for most WB and EB walleye despite stock-specific migration patterns. This study provides novel information about the space use patterns of one of the most economically important fish in North America at spatial and temporal scales relevant to management.
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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 | 0.001 | 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) | 0.001 | 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".