Effects of colonizing predators on yellow perch (<i>Perca flavescens</i>) populations in lakes recovering from acidification and metal stress
Bibliographic record
Abstract
We examined the effects of predator species on yellow perch (Perca flavescens) from lakes in the Sudbury mining region (Ontario, Canada), where fish communities are just beginning to recover from the effects of decades of acidification and metal stress. The predation regimes investigated included six lakes with no predators, four lakes recently invaded (≤3 years) by predators, and eight lakes with well-established (≥15 years) predator populations. Netting survey results indicated that yellow perch biomass was 69% lower in lakes with predators. In lakes recently invaded by predators, perch displayed earlier ontogenetic diet shifts from zooplankton to benthos and much poorer growth during their first growing season. Energy investment (lipid content per egg) in eggs by mature females was also less in the lakes recently invaded by predators. Growth potential in adult perch (age 1), estimated by nucleoside diphosphokinase, was highest in lakes with established predators. Body condition, controlling for size of perch, was also greatest in established predator lakes. Perch body shape in lakes with predators showed a shift from a typical streamlined, pelagic body form towards a deeper-bodied benthic body form, a change that may increase foraging efficiency for benthos or represent an antipredator strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".