Recruitment dynamics of walleyes (<i>Stizostedion vitreum</i>) in Kansas reservoirs: generalities with natural systems and effects of a centrarchid predator
Bibliographic record
Abstract
Knowledge of factors influencing recruitment dynamics of walleyes (Stizostedion vitreum) in different systems and regions is important for developing a better understanding of walleye ecology. Therefore, we investigated associations among walleye recruitment and climatic, water-level, and biotic characteristics in four Kansas reservoirs during 19851999. Walleye recruitment was positively related to spring storage ratios and temperatures and negatively associated with spring water levels and abundance of 130- to 199-mm white crappies (Pomoxis annularis). The influence of juvenile white crappie predation on larval walleyes was examined by conducting a manipulative experiment. Regardless of zooplankton density or water clarity, mortality of larval walleyes resulting from white crappie predation was over 90%. Based on our empirical and experimental results, we propose a bioticabiotic confining hypothesis (BACH) to explain abiotic and biotic effects on walleye recruitment dynamics. Specifically, high variability in walleye recruitment was observed during years with low densities of 130- to 199-mm white crappies and likely resulted from the effects of abiotic factors. When white crappie abundance was high, walleye recruitment was low and exhibited little variability, suggesting that white crappies can have an overriding influence on walleye recruitment regardless of abiotic conditions.
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 | 0.000 | 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".