Light and Temperature: Key Factors Affecting Walleye Abundance and Production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We used published information to determine optimum light and temperature conditions for walleye Sander vitreus (formerly Stizostedion vitreum ) and then applied this simple niche definition to predict how water clarity, temperature, and bathymetry affect walleye habitat availability. Our model calculated thermal–optical habitat area (TOHA), the benthic area of a lake that supplies optimum light, and temperature conditions for walleye during an annual cycle. When water clarity is very low, little walleye habitat exists. As water clarity increases, TOHA for walleye initially increases and then declines exponentially. Optimum water clarity increases with maximum depth of the lake or, in the case of thermally stratified lakes, with thermocline depth. We tested this model by evaluating its ability to account for differences in the sustained yields of walleye fisheries on Ontario lakes. Our results demonstrate that (1) walleye harvest increases in proportion to TOHA times the square root of total dissolved solids, an index of nutrient level, and (2) optimum water clarity for walleye typically exists when Secchi depth is on the order of 2 m. These findings indicate that the increases in water clarity recently observed in the Great Lakes basin (as a result of phosphorus control and dreissenid mussel invasion) have reduced the supply of thermal–optical walleye habitat and, consequently have probably had negative effects on walleye production.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it