Internal seiches as drivers of fish depth use in lakes
Bibliographic record
Abstract
Abstract Inland temperate lakes undergo various physical processes, such as thermal stratification, that dictate the spatial availability of suitable temperature and dissolved oxygen conditions. Here, we use intensive limnological monitoring and acoustic telemetry transmitters implanted in wild fish to document the magnitude and frequency of thermocline deflection events (i.e., wind driven internal seiches that lead to upwelling of hypoxic hypolimnetic water) and their influence on freshwater fish depth use in a coastal embayment of Lake Ontario. The embayment experienced around 100 internal seiche events during a 3‐month period and tracking of walleye (Stizostedion vitreus) vertical positions in the water column showed clear trends of avoidance of low dissolved oxygen. Quantile regression showed a significant correlation between walleye vertical position and the depth of the 3, 4, and 5 mg L−1 oxyclines, with the 3 mg L−1 oxycline having the largest effect. Upwellings of the hypoxic hypolimnion forced walleye to use the water column above these fluctuating oxyclines (5th percentile, p < 0.001), with 94.2% of detections occurring at depths above the 3 mg L−1 oxycline. Understanding how fish respond to upwelling events (both temperature and oxygen) is important for fisheries assessment, management, and habitat restoration planning as there is clear avoidance of suboptimal oxygen conditions and sampling in the overcrowded fringes of these low‐oxygen zones could artificially inflate population estimates.
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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 | 0.001 | 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".