Do lake-specific characteristics mediate the temporal relationship between walleye growth and warming water temperatures?
Bibliographic record
Abstract
Walleye (Sander vitreus) population declines have been linked to climate change, but it is unclear how the growth of this cool-water species may be affected by warming water temperatures. Because warming rates vary among lakes, it is uncertain whether lake characteristics may mediate the temperature effects on walleye growth or may vary as a result of differences in lake habitat or productivity. In this study, we (i) quantified walleye annual growth from 1983 to 2015 in 61 lakes in midwestern United States; (ii) estimated the relationship between annual early life growth (ω; mm·year–1) and water growing degree days (GDD); and (iii) identified lake characteristics affecting loge(ω)–GDD relationships. On average, ω estimates significantly increased with increasing GDD; however, this relationship varied in direction and magnitude among lakes. We estimated an 84% posterior probability of a negative effect of water clarity on the loge(ω)–GDD relationship, suggesting that water clarity may mediate the effect of warming water temperatures by affecting the magnitude and direction of the loge(ω)–GDD relationship. Our results provide insights into the conservation of cool-water species in a changing environment and identify lakes characteristics in which walleye growth may be more resilient to climate change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".