Impacts of changing food webs in Lake Ontario: Implications of dietary fatty acids on growth of Alewives
Bibliographic record
Abstract
Declines in the abundance and condition of Great Lakes Alewives have been reported periodically during the last two decades, and the reasons for these declines remain unclear. To better understand how food web changes may influence Alewife growth and Wisconsin growth model predictions, we fed Alewives isocaloric diets high in omega-6 fatty acids (corn oil) or high in omega-3 fatty acids (fish oil). Alewives were fed the experimental diets at either 1% (“low ration”) or 3% (“high ration”) of their wet body weight per day. After six weeks, Alewives maintained on the high ration diets were significantly larger than those fed the low ration diets. Moreover, Alewives given the high ration fish oil diet were significantly larger than those maintained on the high ration corn oil diet after six weeks of growth. Body lipid, energy density and total body energy of Alewives on the high ration diets were significantly higher than those fed the low ration diets, and total body energy was significantly higher in Alewives given the high ration fish oil diet compared to those on the high ration corn oil diet. The current Wisconsin bioenergetics model underestimated growth and overestimated food consumption by Alewives in our study. Alewife thiaminase activity was similar among treatment groups. Overall, our results suggest that future food web changes in Lake Ontario, particularly if they involve decreases in the abundance of lipid rich prey items such as Mysis, may reduce Alewife growth rates and total body energy due to reductions in the availability of dietary omega-3 fatty acids.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".