Application of X-ray Photoelectron Spectroscopy (XPS) to Assess Soil Organic Matter Under Different Land Uses
Bibliographic record
Abstract
Soil carbon sequestration is suggested as a mechanism to remove CO2 from the atmosphere; however, uncertainty persists regarding the residence time of the stored carbon. Different land uses on the same soil series were selected to compare the amount and properties of soil carbon present and to evaluate the soil-sorbed carbon by loss on ignition estimation and X-ray photoelectron spectroscopy. Surface horizon soil samples were collected from a second-growth cedar forest, mowed grassland, hybrid poplar forest, perennial strawberry field, and an annually cropped wheat field at Totem Field at the University of British Columbia in Vancouver, British Columbia. Soils were analyzed using loss on ignition (LOI), X-ray diffraction (XRD), and angle-resolved X-ray photoelectron spectroscopy (XPS). Results indicate high variability in soil properties and carbon storage across different land uses. Specifically, perennial vegetation exhibited lower soil bulk density and higher soil carbon content compared to agriculturally managed fields, correlating with differences in soil pH. XPS indicated major differences in the amount of C-C and C=O bonds and minor differences in the amount O-C=O and Pi-Pi bonds associated with soil in the different land uses. This study contributes valuable insights that help to inform the relationship between land use practices and soil carbon storage potential.
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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 | 0.001 | 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".