Elevated salinity and water table drawdown significantly affect greenhouse gas emissions in soils from contrasting land-use practices in the prairie pothole region
Bibliographic record
Abstract
Abstract Land-use practices can alter shallow groundwater and salinity, further impacting greenhouse gas (GHG) emissions, particularly in the hydrologically dynamic riparian zones of wetlands. Emissions of CO2, CH4, and N2O were estimated in soil cores collected from two prairie pothole region (PPR) sites with three adjacent land-use practices (i.e., annual crop = AC, pasture = PA, and short rotation willow = SRW) and treated with declining water table depths (2 to 26 cm), and salinity (S0 = control, S1 = 6 mS cm− 1, and S2 = 12 mS cm− 1) in a microcosm experiment. Land-use practices significantly (p < 0.001) affected GHG emissions in soils from both sites in the order of PA > AC = SRW. Compared to the control, emissions of CO2 and CH4 were significantly lower under higher salinity treatments (i.e., S1 and S2), while N2O was significantly higher (p < 0.05). Emissions under declining groundwater table depths were significantly (p < 0.001) variable and specific to each gas, indicating the impacts of shifted soil moisture regime. Overall, the CO2 and CH4 emissions increased up to week four and then decreased with declining water table depths, whereas N2O emission increased up to a maximum at week six. The soils from SRW had considerably lower global warming potential compared to AC and PA. Groundwater salinity in soils from contrasting land-use in the PPR has significant impacts on GHG emissions with potential for crucial climate feedback; however, the magnitude and direction of the impacts depend on hydrology.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".