Modeling turbidity type and intensity effects on the growth and starvation mortality of age-0 yellow perch
Bibliographic record
Abstract
We sought to quantify the possible population-level influence of sediment plumes and algal blooms on yellow perch (Perca flavescens), a visual predator found in systems with dynamic water clarity. We used an individual-based model (IBM), which allowed us to include variance in water clarity and the distribution of individual sizes. Our IBM was built with laboratory data showing that larval yellow perch feeding rates increased slightly as sediment turbidity level increased, but that both larval and juvenile yellow perch feeding rates decreased as phytoplankton level increased. Our IBM explained a majority of the variance in yellow perch length in data from the western and central basins of Lake Erie and Oneida Lake, with R2values ranging from 0.611 to 0.742. Starvation mortality was size dependent, as the greatest daily mortality rates in each simulation occurred within days of each other. Our model showed that turbidity-dependent consumption rates and temperature are key components in determining growth and starvation mortality of age-0 yellow perch, linking fish production to land-based processes that influence water clarity. These results suggest the timing and persistence of sediment plumes and algal blooms can drastically alter the growth potential and starvation mortality of a yellow perch cohort.
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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 | 0.001 | 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".