<sup>13</sup>C CP/MAS-NMR spectra of organic matter as influenced by vegetation, climate, and soil characteristics in soils from Murcia, Spain
Bibliographic record
Abstract
Soils in southern Spain are low in organic matter (OM) and nutrients. Understanding the nature and dynamics of OM has potential to improve soil management technologies for sustainable crop production. The objective of this work was to establish the distribution of functional groups in organic-C from these soils using 13C CP/MAS-NMR spectroscopy and to investigate the influence of vegetation, climatic conditions, soil parameters, parent material, and soil order on these functional groups. No statistically significant variability in the distribution of organic-C groups was found as a result of the influence of either soil order or parent material. The content of O-alkyl-C in the soils under the Rhamno-Quercetum plant community was higher (95% probability) than in the soils under the Paronychio-Sideritidetum plant community. Soils located in the mesomediterranean climatic zone displayed a higher content of O-alkyl-C and a lower content of aromatic-C compared to the soils located in the thermomediterranean zone. These differences were statistically significant at 95% of probability. Vegetation and climatic conditions appear to play a major role in the OM decomposition processes in this region. Statistically significant and positive correlations were found between alkyl-C and both cation exchange capacity (CEC) and clay content indicating the recalcitrant nature of these organic compounds. Key words: Organic matter composition, Spanish soils, 13C CP/MAS-NMR spectra, Mediterranean soils, Alfisols, Mollisols, Aridisols
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 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) | 0.001 | 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".