Latitudinal Comparisons of Walleye Growth in North America and Factors Influencing Growth of Walleyes in Kansas Reservoirs
Bibliographic record
Abstract
Abstract We compared the growth of walleyes Stizostedion vitreum in Kansas to that of other populations throughout North America and determined the effects of the abundance of gizzard shad Dorosoma cepedianum and temperature on the growth of walleyes in Kansas reservoirs. Age was estimated from scales and otoliths collected from walleyes (N = 2,072) sampled with gill nets from eight Kansas reservoirs during fall in 1991-1999. Age-0 gizzard shad abundance was indexed based on summer seining information, and temperature data were obtained from the National Oceanic and Atmospheric Administration. Parameter estimates of von Bertalanffy growth models indicated that the growth of walleyes in Kansas was more similar to that of southern latitude populations (e.g., Mississippi and Texas) than to that of northern (e.g., Manitoba, Minnesota and South Dakota) or middle latitude (e.g., Colorado and Iowa) populations. Northern and middle latitude populations had lower mean back-calculated lengths at age 1, lower growth coefficients, and greater longevity than southern and Kansas populations. A relative growth index (RGI; (Lt /Ls) × 100, where Lt is the observed length at age and Ls is the age-specific standard length derived from a pooled von Bertalanffy growth model) and standardized percentile values (percentile values of mean back-calculated lengths at age) indicated that the growth of walleyes in Kansas was above average compared with that of other populations in North America. The annual growth increments of Kansas walleyes were more variable among years than among reservoirs. The growth increments of age-0 and age-1 walleyes were positively related to the catch rates of gizzard shad smaller than 80 mm, whereas the growth of age-2 and age-3 walleyes was inversely related to mean summer air temperature. Our results provide a framework for comparing North American walleye populations, and our proposed RGI provides a simple, easily interpreted index of growth.
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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 | 0.001 | 0.001 |
| 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".