FA determination in cold water marine samples
Bibliographic record
Abstract
The determination of FA in cold water marine samples is challenging because of the presence of large proportions of a variety of labile PUFA. This study was undertaken to establish optimal methods for FA analysis in various sample types present in the marine environment. Several techniques used in FA analysis, including lipid fractionation, FAME formation, and picolinyl ester synthesis, were examined. Neutral lipids, acetone-mobile polar lipids, and phospholipids (PL) were readily separated from each other on columns of activated silica gel, but recoveries of PL were reduced. Deactivation of the silica gel with 20% w/w water produced variable recoveries of PL (66 +/- 22%). FAME formation with BF3 gave optimal recoveries, and a method to remove hydrocarbon contamination from these samples before GC analysis using column chromatography was optimized. Picolinyl derivatives of FA are useful in structural determinations with MS, and a new base-catalyzed transesterification method of their synthesis from FAME was developed. Finally, a series of calculations, combining FA proportions with acyl lipid class concentrations, was designed to estimate FA concentrations. In algae and animal samples, these estimates were in good agreement with actual FA concentrations determined by internal standards.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".