Growth and survival of age-0 walleye (<i>Sander vitreus</i>): interactions among walleye size, prey availability, predation, and abiotic factors
Bibliographic record
Abstract
We examined the importance of prey availability, predation, and abiotic factors in determining growth and survival of age-0 walleye (Sander vitreus) across 15 Illinois reservoirs during 7 years. Multiple life stages were examined by stocking walleye at three different size groups: larval (6 mm total length (TL)), small (46 mm TL), and large (100 mm TL). Factors affecting growth and survival of walleye varied depending on walleye size. Growth of small and large walleye increased with benthic invertebrate density. Temperature had a positive effect on larval and small walleye growth but a negative effect on large walleye growth. Prey availability was an important factor for walleye survival across all size groups, whereas temperature affected only larval and large walleye. Juvenile centrarchid density had a negative effect on larval walleye survival, presumably caused by predation. Our best predictive models explained substantial variation in survival for larval (97%), small (57%), and large (83%) walleye. We also explained a high proportion of variation in growth of large (98%), small (55%), and larval (52%) walleye. Our study demonstrates the importance of examining multiple life stages to predict growth and survival and leads to a better understanding of walleye recruitment and recommendations for stocking strategies.
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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 | 0.000 | 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".