Do models parameterized with observations from the system predict larval yellow perch (<i>Perca flavescens</i>) growth performance better in Lake Erie?
Bibliographic record
Abstract
Growth performance can influence survival during early life. As such, a range of statistical to mechanistic modeling approaches has been used to predict growth performance, with few studies evaluating prediction accuracy. We tested the ability of three models to estimate observed larval yellow perch (Perca flavescens) growth and length in western Lake Erie (United States – Canada). We found that a general linear model developed using yellow perch data from western Lake Erie performed best followed closely by a semimechanistic individual-based model (IBM) specific to Lake Erie yellow perch and worse by a general multispecies IBM. We suspect that the statistical model performed better because, unlike IBMs, it does not require prey availability data, probably poorly represented by zooplankton samples, and because the IBMs are imperfectly parameterized. Our findings indicate that caution should be exercised when using general IBMs given that the models parameterized with observations from the system of interest outperformed the general IBM in providing accurate fish growth and length estimates, pointing to the need for research that can improve existing mechanism-based models of larval 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".