Game over for gamefishes after zebra and quagga mussel invasion: not in prairie lakes?
Bibliographic record
Abstract
Mitigating threats of aquatic invasive species in lakes requires holistic insights into the biological, physical, and chemical conditions sustaining fish communities. This study establishes important trophic baseline conditions for three key gamefish species—walleye, northern pike, and yellow perch—across 15 hardwater prairie lakes in south-central Saskatchewan, Canada, that are threatened by zebra mussel invasion. We employed stomach content analysis to evaluate diet and ontogenetic nutritional shifts in adult fishes and assessed how fish size and environmental factors influence body condition. Adult walleye transition from a diet of amphipods to fish as they grow, whereas perch and pike maintain a stable diet of invertebrates and fish, respectively. While walleye and pike decline in body condition with size, perch maintain consistently high condition. Lake productivity and alkalinity are key predictors of adult walleye and perch condition. Yet, zebra mussel invasion in hardwater lakes may not have the detrimental impacts observed elsewhere, assuming management strategies address the combined threats of invasion and climate change. These findings provide a foundation for assessing shifts in foraging ecology in yet-to-be-invaded prairie lakes.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".