Natural Isotopic Distribution in Soil Surface Horizons Differentiated by Vegetation
Bibliographic record
Abstract
The isotopic composition of soil organic matter (SOM) is a useful tool for deciphering the different mechanisms underlying decomposition processes in soils. The objective of this study was to quantify the influence of oak ( Quercus dumosa Nutt.) and pine ( Pinus coulteri D. Don) vegetation on the isotopic variation occurring during decomposition by measuring δ 13 C and δ 15 N in selected litter and soil fractions. Soil samples obtained from A horizons of two lysimeter soils were separated by density and mineral size to isolate the floatable, fine silt, and clay fractions. These fractions as well as the litter samples were subjected to sequential chemical extractions to differentiate between polar and nonpolar extractives, acid‐soluble carbohydrates, and acid‐insoluble residues. The physical fractions varied by up to 3.5‰ for δ 13 C and 4.7‰ for δ 15 N, while acid‐insoluble residues were depleted by 0.9 to 2.1‰ δ 13 C as compared with the samples before extraction. Under oak, 13 C and 15 N content progressively increased from the litter to the floatable, fine silt, and clay fractions (by 4.7‰ for δ 13 C and 4.9‰ for δ 15 N). By comparison, under pine, enrichment of the clay fraction was 1.7‰ for δ 13 C and 1.7‰ for δ 15 N as compared with the initial litter. The greater enrichment in heavy isotopes under oak vegetation as compared with the pine could not be explained based on differences in litter inputs. Results suggested instead that variation in decomposition processes by vegetation type caused the differences in heavy isotope enrichment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".