Among- and within-species variability in fatty acid signatures of marine fish and invertebrates on the Scotian Shelf, Georges Bank, and southern Gulf of St. Lawrence
Bibliographic record
Abstract
The fat and fatty acid compositions of 28 species of fish and invertebrates (n = 954) from the Scotian Shelf, Georges Bank, and the Gulf of St. Lawrence were determined. Discriminant analysis of the 16 most numerous species (n [Formula: see text] 18 each), using 17 major fatty acids, classified species with greater than 98% accuracy and grouped species into three general clusters (gadids, flatfish, and planktivores) with similar fatty acid compositions, and likely, similar diets. A number of species exhibited changes in fatty acid signatures with increasing size (multivariate analysis of variance), which corresponded with known dietary shifts reported from stomach contents analyses. Location effects were also observed among the three major geographical regions and were probably due to broad-scale variations in prey assemblages and phytoplankton composition in the northwestern Atlantic. Despite these effects, within-species variation was still substantially less than among-species variation. Thus, fatty acid signatures can be used to distinguish and characterize fish and invertebrate species in a given ecosystem, as well as to study finer-scale trophic interactions of these species. These data also have applications at higher trophic levels and will serve as a prey database for studying the diets of other fish and marine mammal predators using fatty acid signatures.
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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".