Does hypoxia reduce habitat quality for Lake Erie walleye (<i>Sander vitreus</i>)? A bioenergetics perspective
Bibliographic record
Abstract
In Lake Erie, the duration and extent of hypoxia (dissolved oxygen (DO) ≤ 2 mg·L–1) has increased in recent years, yet little is known on the corresponding impact on its fish, particularly the highly valued walleye ( Sander vitreus ) fishery. Here, we quantified the impact of hypoxia on walleye habitat quality, using a spatially explicit growth rate potential (GRP) modeling approach, which integrates the spatial arrangement of biological (prey availability) and environmental (DO, temperature, irradiance) measures. Data were collected along two types of transects: 60 km north–south transects (each sampled once during day and night) and 5 km east–west transects (sampled every 4 h for 24 h) during August (pre-hypoxia), September (peak-hypoxia), and October (post-hypoxia) 2005. Overall, the average monthly amount of high quality habitat (GRP > 0 g·g–1·day–1) for walleye declined slightly with hypoxia (<2.0%); however, hypoxia appeared to enhance habitat quality by concentrating prey in favorable temperature, DO, and light conditions. In September, percentages of walleye growth rates were at the upper end of the range, much more so than during August or October. Although an understanding of walleye distributions, foraging, and growth in relation to hypoxia is needed, our results do not suggest that hypoxia is negatively influencing walleye through reduced habitat quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".