Response of CO2, N2O, and CH4 fluxes to contour tillage, diversion terrace, grassed waterway, and tile drainage implementation
Bibliographic record
Abstract
In this study we evaluated CO2, N2O, and CH4 fluxes in two integrated best management practices (BMPIs) comprised of the following individual practices: diversion terraces (DT), grassed waterways (GW), and contour tillage (CT) [i.e., DTGW]; and DT, GW, CT, and tile drainage (TD) [i.e., DTGW+TD], relative to CT that served as a control. It was anticipated that due to its effects on soil water redistribution and soil temperature, diversion terraces and grassed waterways would influence the pattern of greenhouse gas (GHG) emission. This is the first study in the world linking such erosion control structures with subsurface drainage. Cumulative CO2 emissions were greatest in DTGW in both 2020 and 2021. In 2019, DTGW+TD N2O emissions were significantly lower than CT and DTGW. N2O emissions were highest in DTGW in 2020 and 2021, though not statistically significant. There were no significant differences in CH4 in any year. Soil in all BMPIs acted as a weak CH4 sink during the study period. This study demonstrated that the addition of TD to DT and GW significantly reduced the loss of stored carbon (as CO2) relative to undrained DT and GW, while also not emitting significantly more carbon than CT, in the initial years after implementation. Results were similar with respect to the loss of nitrogen, as N2O, where undrained DT and GW generally emitted more N2O in the first years after implementation.
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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 | 0.001 |
| 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 | 0.001 | 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".