Profiling neutral lipids in individual fish larvae by using short‐column gas chromatography with flame ionization detection
Bibliographic record
Abstract
The triacylglycerol (TAG) to sterol (ST) ratio, which has been used to determine the condition index in marine species, has regularly been measured via thin‐layer chromatography with flame ionization detection (TLC/FID) using the Chromarod‐Iatroscan system. However, this method is labor intensive, requires long analysis times, and has a detection limit of ~50 ng. For the determination of lipids in very small samples such as individual fish larvae, short‐column gas chromatography with flame ionization detection (GC/FID) provides an excellent alternative to Iatroscan TLC/FID, owing to its lower detection limit (~1 ng for high‐molecular‐weight TAG and ~0.1 ng for lower‐molecular‐weight species). As well, GC/FID individually profiles lipids based on their carbon number, whereas TLC/FID groups lipids according to their lipid class. Here we describe a method for the determination of neutral lipids from individual Cyclopterus lumpus (lumpfish) and Myoxocephalus scorpius (short‐horn sculpin) larvae using short‐column GC/FID. By using an internal standard and applying weight correction factors, ST and TAG can be accurately and precisely measured. The results reveal that there were no significant differences between GC/FID and TLC/FID in the quantification of TAG or ST in individual fish larvae (P > 0.05); however, GC/FID is more sensitive, precise, rapid, and cost‐efficient.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".