XANES and Pyrolysis-FIMS Evidence of Organic Matter Composition in a Hummocky Landscape
Bibliographic record
Abstract
The quantity and quality of soil organic N and C are strongly influenced by variations in landscape position and management practices such as cultivation. In this study, we used synchrotron-based C and N K-edge x-ray absorption near-edge structure (XANES) spectroscopy coupled with pyrolysis–field ionization mass spectrometry (Py-FIMS) to explore C and N chemistry among soils obtained from different landscape positions along a hummocky transect and from locations under different management practices. A distinct landscape pattern in soil organic matter (SOM) functionality was observed, but only a small effect of management (cultivated vs. uncultivated) was detected. We observed that both carbohydrates and low-molecular-weight aromatics, which are usually considered readily degradable, were apparently stabilized in divergent (i.e., water-shedding) slope positions, while lipids and high-molecular-weight lignins were enriched in depressional areas. We posit that tillage incorporation was probably responsible for the enrichment of older, microbially altered carbohydrates and lignins at divergent positions. Using novel application of nonmetric multidimensional scaling ordination to XANES data, we observed more heterocyclic N compounds in cultivated vs. uncultivated soils. The XANES data also showed the presence of unique, oxidized N-bonded aromatics, which predominated at calcareous divergent slope positions. These types of organic N compounds are rarely reported in the literature except for samples obtained in acidic or anaerobic environments. This study provides analytical evidence that C and N K-edge XANES and Py-FIMS differentiate SOM composition at the molecular level from soils obtained from diverse topographic positions and management regimes in a hummocky prairie landscape. Results provide complementary evidence that SOM comprises more stabilized compounds at divergent slope positions and more active compounds at depressional positions.
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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".