Identification and quantification of important steryl esters in aspen wood
Bibliographic record
Abstract
Abstract Steryl esters make up a major portion of the total lipids in aspen wood, and contribute significantly to pitch deposit problems during pulping. Fungal treatment of aspen is an attractive method for removing these compounds because it is inexpensive and environmentally acceptable; however, the mechanism of steryl ester removal remains unclear. Identification of the steryl esters will lead to a better understanding of how they are removed by fungi. The steryl ester fraction from aspen wood was obtained by acetone extraction then further purified by silica gel column chromatography and argentation‐silica gel column chromatography. This led to the isolation of three major fractions: fraction I, fraction II, and fraction III. The major steryl esters of fractions I and II were identified by gas chromatography, gas chromatography‐mass spectroscopy, and proton nuclear magnetic resonance analysis of the intact fraction as well as sterol and fatty acid moieties obtained after base hydrolysis. Identification of the steryl esters was carried out by mass spectra comparisons with steryl ester standards synthesized in the laboratory and comparison with mass spectra libraries (Wiley and NIST) by mass fragmentography. Fraction I contained primarily the palmitate, stearate, and eicosanoate esters of α‐ and β‐amyrin. Fraction II consisted mainly of the palmitate, stearate, and eicosanoate esters of tirucalla‐7,24‐dien‐3β‐ol and lupeol.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".