Mass spectrometry of organic matter influenced by long-term pedogenesis and a short-term reclamation practice in an Oxisol of Brazil
Bibliographic record
Abstract
Little is known about the influence of soil pedogenesis and reclamation practices on the chemical composition of soil organic matter (SOM) in eroded Oxisol. We examined the long-term influence of pedogenesis and 8 years of a reclamation practice on SOM in the top 5 cm of an artificially eroded Oxisol of Brazil. The experimental site involved replicated treatments established under native vegetation, and an adjacent site whose top 8.6 m had been removed mechanically (eroded reference). The eroded Oxisol was under reclamation with native tree and grass species, and addition of sewage sludge. Pyrolysis field ionization mass spectrometry was used to characterize SOM. The abundance of most classes of SOM and soil carbon decreased in the following order: native > reclaimed >> eroded soil. Relative to the eroded reference, SOM in the native soil was highly humified and stabilized by inorganic colloids of iron, aluminum, and silicon. Humified and thermally stable SOM in the native and reference eroded soils involved mostly alkylaromatics, lipids, phenols+lignin monomers, lignin dimers, and N-heterocyclics. The reclaimed soil SOM was less humified and less stable than the native Oxisol, showing significant contributions of carbohydrates, amino acids, and sterols derived from sewage sludge and plant residues.
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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 | 0.001 | 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) | 0.000 | 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".