Quality-assurance study of marine lipid-class determination using chromarod/iatroscan® thin-layer chromatography-flame ionization detector
Bibliographic record
Abstract
Abstract An Iatroscan® thin-layer chromatography–flame ionization detector has been utilized to quantify lipid classes in marine samples. This method was evaluated relative to established quality-assurance (QA) procedures used for the gas chromatographic analysis of PCBs. A method for extracting and analyzing eight major lipid classes in the ribbed mussel (Guekensia demissus) was developed. The analytical method met the QA criteria prescribed for consistent external calibrations, low blanks, complete extraction of all lipid classes, and precise replicate analysis. Matrix and blank spikes were satisfactorily recovered (50-130%), provided that the samples contained a large enough mass (>4% dry weight) of total lipids to overcome the absorption of polar lipids on glassware. The use of frozen mussel homogenate as a standard reference material was not possible because of lipid degradation, particularly of triacylglycerols and phospholipids. Also, total lipids measured gravimetrically significantly decreased in frozen samples, which could influence bioaccumulation predictions. A laboratory intercalibration was performed using a mussel homogenate and chloroform extract, which verified the accuracy of the method and the lipid-class identification. Characterizing the structure of one class of polar lipids, the acetone mobile polar lipid (AMPL), showed that it contained no ester linkages or free/sterically unhindered -OH groups; however, the AMPL did contain an ether linkage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".