Energy acquisition and allocation patterns of lake whitefish (<i>Coregonus clupeaformis</i>) are modified when dreissenids are present
Bibliographic record
Abstract
We evaluated the effects of dreissenid-induced food web changes on rates of lake whitefish ( Coregonus clupeaformis ) energy acquisition and allocation in North American populations. We used mass-balance models of lake whitefish growth and methylmercury accumulation in 17 populations with and without dreissenids present to estimate and contrast rates of activity (ACT), consumption (C) and conversion efficiency (V). Historical estimates were also generated for a single lake whitefish population during dreissenid establishment. Bioenergetic estimates from both scenarios were compared with densities of Diporeia , a historically important diet component of lake whitefish. Mean lake whitefish ACT and C estimates in populations with dreissenids were significantly greater: 1.3–2 times those of populations without dreissenids. Conversion efficiencies scaled positively and significantly, while C and ACT varied negatively and significantly with Diporeia abundance. Our results suggest that changes in lake whitefish activity may affect density estimates — and ultimately sustainable management quotas — for this species. Our results also show that reported declines in lake whitefish individual growth rates in South Bay, Lake Huron, can be explained by increased activity rates due to increased foraging activity in an energetically depleted prey community.
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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 | 0.000 |
| 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) | 0.000 | 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".