Interactive effects of elevated CO<sub>2</sub>, N deposition and climate change on extracellular enzyme activity and soil density fractionation in a California annual grassland
Bibliographic record
Abstract
Abstract Elevated CO2, N deposition and climate change can alter ecosystem‐level nutrient cycling both directly and indirectly. We explored the interactive effects of these environmental changes on extracellular enzyme activity and organic matter fractionation in soils of a California annual grassland. The activities of hydrolases (polysaccharide‐degrading enzymes and phosphatase) increased significantly in response to nitrate addition, which coincided with an increase in soluble C concentrations under ambient CO2. Water addition and elevated CO2 had negative but nonadditive effects on the activities of these enzymes. In contrast, water addition resulted in an increase in the activities of lignin‐degrading enzymes (phenol oxidase and peroxidase), and a decrease in the free light fraction (FLF) of soil organic matter. Independent of treatment effects, lignin content in the FLF was negatively correlated with the quantity of FLF across all samples. Lignin concentrations were lower in the aggregate‐occluded light fraction (OLF) than the FLF, and there was no correlation between percent lignin and OLF quantity, which was consistent with the protection of soil organic matter in aggregates. Elevated CO2 decreased the quantity of OLF and increased the OLF lignin concentration, however, which is consistent with increased degradation resulting from increased turnover of soil aggregates. Overall, these results suggest that the effects of N addition on hydrolase activity are offset by the interactive effects of water addition and elevated CO2, whereas water and elevated CO2 may cause an increase in the breakdown of soil organic matter as a result of their effects on lignin‐degrading enzymes and soil aggregation, respectively.
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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".