Short-term response of soil aggregate stability and labile carbon to contour tillage, diversion terrace, grassed waterway, and tile drainage implementation
Bibliographic record
Abstract
Soil degradation has been of great concern for New Brunswick's potato farmers, especially on sloped land and shallow soils. In this study, we evaluated the initial response of labile soil carbon (C) fractions (permanganate oxidizable C (POXC) and particulate organic C (POC)) and aggregate stability to two integrated best management practices (BMPIs) composed 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. The more water was regulated in the field, the greater the increase in labile C; where DTGW and DTGW + TD gained 19.8% and 50.6% of POXC, respectively, while CT lost 11.2% of POXC. There was an increase in mineral associated organic matter C in the terraced BMPIs, despite the high amount of tillage events that took place during potato cultivation. Two BMPIs had no effect on aggregate stability, most likely due to the short duration of this initial monitoring study that spanned just over two growing seasons. Even though there were no improvements in soil structure, our findings showed that some stabilization of soil C is possible even during the initial two seasons following BMPI 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 | 0.000 | 0.000 |
| 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".