Response of CO2, N2O, and CH4 fluxes to contour tillage, diversion terrace, grassed waterway, and tile drainage implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this study we evaluated CO 2 , N 2 O, and CH 4 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 CO 2 emissions were greatest in DTGW in both 2020 and 2021. In 2019, DTGW+TD N 2 O emissions were significantly lower than CT and DTGW. N 2 O emissions were highest in DTGW in 2020 and 2021, though not statistically significant. There were no significant differences in CH 4 in any year. Soil in all BMPIs acted as a weak CH 4 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 CO 2 ) 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 N 2 O, where undrained DT and GW generally emitted more N 2 O in the first years after implementation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 it