Evaluating quantitative fatty acid signature analysis (QFASA) in fish using controlled feeding experiments
Bibliographic record
Abstract
Accurate diet estimation has long been a challenging issue for researchers investigating predators because of constraints associated with stomach content analyses. Fatty acid signature analysis offers an alternative avenue to study long-term diet trends in consumers. Despite the wealth of experiments involving fatty acids of fish and their diets, few have evaluated quantitative fatty acid signature analysis (QFASA) with fish consumers. To this end, we fed juvenile lake trout (Salvelinus namaycush), round goby (Neogobius melanostomus), and yellow perch (Perca flavescens) various invertebrate species and back-classified each predator to its respective prey using only fatty acids. Estimates were highly accurate when metabolism of diets was natively accounted for by using fatty acid profiles of predators fed known diets as the “prey library”. While highly accurate results were obtained, accounting for each predator–prey relationship limits the use of QFASA to predators that consume a limited number of species. We call for specific knowledge as to how fatty acid profiles reflect each predator–prey interaction before attempting to use fatty acids to quantify a consumer’s diet. Only after incorporating such data will QFASA provide an accurate view of individual’s diets when stomach content data are not available or are invalid.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".