Effects of calcium treatment on forest floor organic matter composition along an elevation gradient
Bibliographic record
Abstract
Calcium amendment is a restorative option for nutrient-depleted, acidic soils in the forests of the northeastern United States. We studied the effects of watershed-scale wollastonite (CaSiO3) application on the structural composition of soil organic matter (SOM) and hot-water-extractable organic matter (HWEOM) at the Hubbard Brook Experiment Forest in New Hampshire 7–9 years after treatment, along an elevation gradient. Soils in the high-elevation spruce–fir–birch (SFB) zone contained significantly greater amounts of HWEOM compared with lower elevation hardwood soils, likely due to differences in litter quality and slower decomposition rates in colder soils at higher elevation. The only significant difference in hot-water-extractable organic carbon concentration between reference and calcium-treated watersheds was in Oie horizons of the SFB zone, which also exhibited the greatest degree of soil chemical change after treatment. The 13C nuclear magnetic resonance (NMR) spectra showed no significant patterns in O-alkyl C abundance for either soil or HWEOM along the elevation gradient, suggesting that there were no elevation-related patterns in carbohydrate concentration. The general absence of long-term effects in this study suggests that effects of Ca amendment at this dosage on the composition of SOM were small or short-lived.
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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) | 0.001 | 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".