Nature of organic carbon and nitrogen in physically protected organic matter of some Australian soils as revealed by solid-state <i>13</i> C and <i>15</i> N NMR spectroscopy
Bibliographic record
Abstract
The &lt;53-□m particle-size fractions of 5 different Australian soils were subjected to high energy ultraviolet (UV) photo-oxidation for a period of 2 h in order to remove most of the physically unprotected organic material. Solid-state 13C and 15N nuclear magnetic resonance (NMR) spectroscopy was applied for characterising the chemical nature of the remaining organic fraction. The 13C NMR spectroscopic comparison of the residues after UV photo-oxidation and the untreated bulk soils revealed a considerable increase in condensed aromatic structures in the residues for 4 of the 5 soils. This behaviour was recently shown to be typical for char-containing soils. In the sample where no char was detectable by NMR spectroscopy, the physically protected carbon consisted of functional groups similar to those observed for the organic matter of the bulk sample, although their relative proportions were altered. The solid-state 15N NMR spectrum from this sample revealed that some peptide structures were able to resist UV photo-oxidation, probably physically protected within the core of microaggregates. Heterocyclic aromatic nitrogen was not detected in this spectrum, but pyrrolic nitrogen was found to comprise a major fraction of the residues after photo-oxidation of the &lt;53-□m fractions of the char- containing soils. Acid hydrolysis of these samples confirmed that some peptide-like material was still present. The identification of a considerable amount of aromatic carbon and nitrogen, assignable to charred material in 4 of the 5 investigated soils, supports previous observations that char largely comprises the inert or passive organic matter pool of many Australian soils. The influence of such material on the carbon and nitrogen dynamics in such soils, however, requires further research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".