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 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 | 0.000 | 0.000 |
| 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".