Influence of lake volume on food web metrics in a freshwater fish assemblage across a small range of ecosystem sizes
Bibliographic record
Abstract
Lake size effects on food webs have been most clearly demonstrated for predator fish across large gradients in ecosystem size and species richness. We isolated the influence of lake size to assess food web metrics in six fish species with diverse ecologies across five lakes with similar lake characteristics in Algonquin Provincial Park (Ontario, Canada) using δ13C, δ15N, and δ34S. Lake volume was a significant factor influencing food web metrics across most fish species. However, relationships between lake volume and food web metrics (trophic position, littoral carbon use, δ34S, and niche area) were weak. For most species, trophic position decreased with lake volume, opposite from previous studies that included a wider range of lake sizes and biodiversity. Littoral carbon use and δ34S showed negative and positive relationships with lake volume, respectively, suggesting a shift to pelagic offshore energy in larger lakes. Albeit weak, our results highlight that multiple co-occurring fish species within a community can have similar responses in littoral carbon use, trophic position, δ34S, and niche area across a small range of lake sizes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".