Lake Whitefish Feeding habits and condition in Lake Michigan
Bibliographic record
Abstract
Lake whitefi sh (Coregonus clupeaformis) have experienced declines in condition in some areas of the Great Lakes. The hypothesis tested was that condition—in terms of relative weight, percent lipid and docosahexaenoic acid (DHA)—was greater in regions where larger proportions of high quality prey (e.g., Diporeia) were included in the diet. Samples of spawning lake whitefi sh from four regions around Lake Michigan (northwest, Naubinway, Elk Rapids and southeast) had distinct mean carbon and nitrogen stable isotope signatures. Lake whitefi sh may be using a variety of prey items, especially the Naubinway population where fi sh occupy the largest stable isotopic niche space. However, trophic niche width inferred from stable isotopes did not vary among regions. Relative weight was highest in the southeast and lowest for all northern regions. The mean measured lipid from lake whitefi sh dorsal, skinless, muscle biopsies were highest for northwest fi sh. DHA was signifi cantly different among regions, with high mean values in Elk Rapids and the northwest. No correlations were found between stable isotope measures and condition metrics. The results suggest that lake whitefi sh are coping with declining Diporeia abundances by feeding on alternate prey. Overall results do not substantiate the hypothesis of a relationship between condition and prey use, although lake whitefi sh from Elk Rapids and the northwest had high quality prey and good condition.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".