Molecular characterization of ombrotrophic peats by humeomics
Bibliographic record
Abstract
Abstract Background An insight into the molecular composition of ombrotrophic peats of different geographical origin and collected at different depths was achieved by the humeomics method. The humeomic fractionation allowed the separation of molecular components in either organic solvents or water on the basis of their progressive binding strength to the humic matrix. The solubilized matter in fractions was analyzed by gas chromatography–mass spectrometry (GC–MS) or by proton nuclear magnetic resonance (1H NMR) spectroscopy, while the residues depleted of the extracted material were observed by 13C cross-polarization magic-angle-spinning nuclear magnetic resonance (13C-CPMAS-NMR) spectroscopy. Results The analytical characterization of fractions and residues differentiated peats not only on the basis of the different classes of extracted molecules, but also on their binding strength to the complex peat matrix. Aromatic, lipidic, and sugar compounds were the most representative molecular classes extracted in the humeomic fractions and their abundance varied with depth. The distribution and abundance of extracted compounds provided an indication of the extent of organic matter accumulation in peat. The NMR spectra of solid residues supported the interpretation of the characteristics of the various extracts. Conclusions Our findings proved that the humeomic approach allows to provide important information on both the molecular composition of peats and its variation with depth.
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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".