Population social structure and gizzard shad density influence the size-specific growth of bluegill
Bibliographic record
Abstract
Many bluegill (Lepomis macrochirus) populations are stunted and consist mainly of smaller individuals. There has been much recent interest in determining factors that influence the growth of bluegill so that management remedies can be designed to alleviate stunting. Bluegill population size structure is unlikely controlled by any one factor. Instead, multiple variables likely interact to regulate adult size. We used Akaike’s information criterion to determine how various environmental variables influence the size-specific growth of bluegill at 50, 100, and 150 mm total length (TL) in 16 lakes. Eight models related to prey availability, lake productivity, lake habitat, predation pressure, intraspecific competition, angling pressure, gizzard shad (Dorosoma cepedianum) density, and population social structure were constructed. Population social structure had the greatest effect on the size-specific growth rates of fish at 50 mm TL. At this size we found a significant negative relationship between size-specific growth rates and the mean age of maturation of males in the population. Size-specific growth rates at 100 and 150 mm TL were negatively related to gizzard shad density. These results suggest that management actions that help to increase the numbers of large males and reduce gizzard shad density would help alleviate stunting in bluegill populations.
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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 | 0.002 |
| 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".