CHANGES IN LIPIDS AND STEROLS DURING COMPOSTING
Bibliographic record
Abstract
Pyrolysis-gas (Py-GC) chromatography was used to characterize organic [(diethyl ether (DEE) and chloroform (CHCl3)] extracts of raw and composted duck excreta enriched wood shavings from two finishing cycles (C1 and C2). Materials were collected on days 0, 8 and 23. C1 contained 1.7 % total N while C2 contained 0.9 % total N. Py-GC-MS (mass spectrometry) showed that the extracts contained n-alkanes (C12 to C32), alkenes (C12:1 to C33:1), n-fatty acids (C12 to C28), unsaturated fatty acids (C18:1 and C18:2), and sterols (cholestene, cholestadiene, stigmastene, stigmastadiene, stigmastatriene, cholesterol, stigmastanol, stigmastanone, stigmastadienone, 17-methyl dialkylsulfanyl decahydro-1H-cyclopenta [a] phenanthrene, 17-methyl dialkylsulfanyl dodecahydro-1H-cyclopenta [a] phenanthrene, and 17-methyl-17-dialkylsulfanyl decahydro-1H-cyclopenta [a] phenanthrene). Other components identified were prystene, squalene (precursor of cholesterol), phthalic acid, diphenylpropane, diphenylbut-2-ene and 1,3,6 triphenyl hex-4-ene. Our data showed significant changes in the lipid composition of duck excreta enriched wood shavings during composting, which appeared to be related to the total N content of the system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".