Can growth be used as a surrogate measure of walleye (<i>Sander vitreus</i>) abundance change?
Bibliographic record
Abstract
We examined several models to determine the relative contributions of density-dependent and density-independent factors on walleye (Sander vitreus) growth in the ceded territory of Wisconsin from 1990 to 1999. We then used independent data from 2000 to determine how well each model predicted walleye growth and examined several models to determine if growth could be used to predict density. Adult density best predicted the mean size of age-3 males ( [Formula: see text]3m) and age-5 females ( [Formula: see text]5f). Density-independent measures of pH, the morphoedaphic index, maximum depth, and conductance had a positive influence on growth, while adult density negatively affected growth. The solely density-dependent models predicted [Formula: see text]3m and [Formula: see text]5f poorly in 2000. The addition of density-independent factors improved model predictions of [Formula: see text]3m and [Formula: see text]5f. Walleye growth did not predict adult walleye densities. Regional patterns in walleye growth were correlated with surrogate measures of lake productivity and to a lesser extent adult density. Density dependence had a relatively weak affect on growth patterns, suggesting that growth cannot be used as a surrogate method for monitoring abundance 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".