Quantitative analysis of fatty acid precursors in marine samples
Bibliographic record
Abstract
To apply fatty acid analyses to the study of foraging ecology and diet determination, all compounds that may be deposited as fatty acids in a predator must be quantified in the prey. These compounds include the usual fatty acids in acyl lipids, but also the alcohols of wax esters and the vinyl ethers of plasmalogens. In routine fatty acid analysis, samples are extracted and transesterified (methylated), resulting in the formation of fatty acid methyl esters (FAMEs); however, fatty alcohols and dimethylacetals (DMAs) are also generated if wax esters or plasmalogens are present. Here, we present a new method using a modified Jones' reagent to oxidize these alcohols and DMAs to free fatty acids (FFAs). These FFAs are then easily methylated and quantitatively recombined with FAMEs from the same sample. This generates a fatty acid signature of prey that is equivalent to that which the predator has available for deposition upon digestion of that prey. This method is validated with alcohol and DMA standards. Its application to typical marine samples is also presented, demonstrating the change in effective fatty acid signature after inclusion of fatty acids derived from wax esters and plasmalogens.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".