The impact of Arsenic induced stress on soil enzyme activity in different rice agroecosystems
Bibliographic record
Abstract
Arsenic (As) contamination was used to stress ecosystem functioning and the activity of five soil enzymes were used to measure the level of stress: β-glucosidase, urease, acid phosphatase, alkaline phosphatase, and arylsulfatase. Two consecutive field experiments were conducted based on differences in rice agroecosystems during the monsoon (wet) and the post-monsoon (dry) seasons: one measured the impact of As stress on anaerobic rice agroecosystem and the other under aerobic; each receiving soil amendments (including organic manure, vermicompost, NPK, silicon, iron). Aerobic treatment significantly reduced soil As (P <0.05) as compared to anaerobic conditions. The activity of β-glucosidase increased the highest under aerobic conditions (30%–34%), ranging from 52.64–194.15 μg ρ-nitrophenol g−1 of dry soil h−1 relative to anaerobic conditions. Enzyme activities also increased under aerobic conditions, ranging from 24%–29%, 21%–22%, 12%–18%, and 14%–16% for alkaline phosphatase, arylsulfatase, acid phosphatase and urease, respectively. The incorporation of organic manure under aerobic conditions resulted in significant increases in enzyme activities relative to control and NPK. Differences in As concentrations between each of the agroecosystem caused significant inhibitory effects on most soil enzyme activities; however, urease activity was not impacted. Principal Component Analysis (PCA) of enzyme activities indicated that urease activity had the lowest factor loading on PCA in wet and dry seasons. Overall, the aerobic system was better to strengthen soil ecosystem health by increasing enzyme activities, while the activity of β-glucosidase, acid phosphatase, alkaline phosphatase and arylsulfatase can serve as potential indicators of soil biochemical functionality in As contaminated soils under study conditions.
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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".