Fatty acid signatures of Lake Michigan prey fish and invertebrates: among-species differences and spatiotemporal variability
Bibliographic record
Abstract
Lipid concentration and fatty acid composition of common prey species or taxonomic groups from four distinct regions of Lake Michigan were quantified (n = 894). We used a combination of parametric and nonparametric statistics to assess the differences in fatty acid signatures (FAS) among species and to evaluate intraspecies variation relative to interspecies variation in FAS. Discriminant function analysis performed on 13 species or taxa groups using the 18 most abundant fatty acids revealed clear separation among taxa, with overall classification success reaching 89%. Species were readily distinguished based on their overall fatty acid profile in spite of intraspecies variation (temporal, regional, and size-related). Among species sampled, pelagic and benthic clusters were formed based on the degree of fatty acid profile similarity. In alewife ( Alosa pseudoharengus ) and round goby ( Neogobius melanostomus ), fatty acid compositions differed with fish size, sampling location, and temporal variation; however, the magnitude of these differences was small relative to differences between species. Our results demonstrate the utility of fatty acid signatures in studies of food webs in large freshwater ecosystems. This study is also a necessary first step toward development of mechanistic research that investigates the effects of variation in fatty acids within the prey base on top predators.
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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 | 0.001 |
| 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) | 0.001 | 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".