Elucidating patterns of size-dependent predation on larval yellow perch (<i>Perca flavescens</i>) in Lake Michigan: an experimental and modeling approach
Bibliographic record
Abstract
Yellow perch (Perca flavescens) in Lake Michigan have experienced sustained recruitment failure since 1990 as a result of increased mortality during the pelagic larval phase. Increased mortality of larval yellow perch has been tied indirectly to increased alewife (Alosa pseudoharengus) predation, but effects of predation on larval survival variability must be better understood. We compared the relative importance of predation by alewife and two other fish predators to larval survival in laboratory experiments and developed an individual-based predation model (IBM) to examine patterns in size-dependent predation vulnerability. Simulations exposing larval perch to predation by all predators suggest that larval mortality resulting from alewife predation is more size-dependent than mortality resulting from the other two predators, and the range of sizes vulnerable to alewife is smaller. Alewife predation may not be an important mortality source for larval yellow perch in Lake Michigan at present because of the narrow range of vulnerable sizes and low densities of larval perch in the open lake. Predation is more likely to be important in smaller, more productive systems where other predators are abundant. Modeling results also indicate IBM analysis of date of hatch distributions of surviving larvae is a valuable tool for identifying factors most important to larval survival.
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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 | 0.001 |
| 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".