Lipid and Fatty Acid Differences in Lake Trout (Salvelinus namaycush) Eggs from the Great Lakes, Cayuga Lake, and Lake Champlain
Bibliographic record
Abstract
The main objectives of this study were to determine and compare fatty acid signatures (FAS) of lake trout eggs within and among the Great Lakes region. Fifteen sites were sampled over 2 years, including six sites in Lake Michigan, four sites in Lake Huron and one site each in Lake Ontario, Lake Superior, Lake Champlain, and Cayuga Lake. A total of 518 egg samples were quantified. A combination of univariate and multivariate statistical analyses was used to assess spatial and temporal differences in FAS in both the neutral lipid (NL) and phospholipid (PL) fractions of lake trout eggs. At each sampling site, FAS did not differ significantly between the 2 years of sampling. Therefore samples from 2009 and 2010 were combined to assess spatial differences. Discriminant factor analysis (DFA) was performed on lake trout eggs from 13 sample sites using 18 of the most abundant fatty acids detected. DFA revealed a clear separation of lake trout eggs by sample site reaching an overall classification success of 77.7% and 77.3% in the neutral lipid and phospholipid fractions, respectively. Similarly, nonmetric multidimensional scaling and SIMPER analyses revealed differences in FAS among sample sites in both lipid fractions. These differences were driven by 16:1n-7 and 18:1n-9 in the NL and by 16:0 and docosahexaenoic acid in the PL. We suggest that the differences observed in FAS in lake trout eggs among sample sites are reflective of the lake trout feeding habit.
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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 | 0.000 |
| 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) | 0.001 | 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".