Soil enzyme activity as affected by land-use, salinity, and groundwater fluctuations in wetland soils of the prairie pothole region
Bibliographic record
Abstract
Abstract Land-use change and climatic variability are significant drivers for the loss of ecosystem services and soil quality in the prairie pothole region (PPR) wetland systems. Land-use induced changes in groundwater table and salinity may influence biogeochemical processes facilitated by extracellular enzymes (EEs) involved in soil organic matter (SOM) decomposition. The effects of changing groundwater table and salinity on β-glucosidase (BG), N-acetyl glucosaminidase (NAG), and alkaline phosphatase (AP) activities were assessed in wetland soils collected from three different adjacent riparian land-use practices in the PPR. In a microcosm study conducted over ten weeks, soils were treated with groundwater salinity (control, 6 mS cm− 1, and 12 mS cm− 1) and declining groundwater table depths. Extracellular enzyme activities (EEAs) differed significantly (p < 0.05) among soils from different land-uses and between groundwater table depths. The impact of groundwater salinity on soil EEAs were non-significant (p > 0.05). Soil EEAs were significantly higher in soils from pasture, suggesting that the land-use effects resulted from background SOC and TN. Soil EEAs significantly (p < 0.05) reduced under a deeper groundwater table depth, except reverse for BG in site B, indicated that the lowered groundwater table could lead to transitory drought stress for SOM decomposers.
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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".