The inclusion of trees and the introduction of non-native earthworms may increase greenhouse gas emissions from riparian buffer strips.  
Bibliographic record
Abstract
Forested riparian buffer strips (FRBS) are common in temperate agroecosystems due to their ability to sequester nutrients from agricultural runoff and to sequester carbon. The full environmental benefits of FRBS can only be evaluated, however, by accounting for a wide range of criteria that go beyond stream water quality. For example, it is important to determine the net greenhouse gas (GHG) balance of FRBS relative to adjacent agricultural fields. It is also important to identify the factors controlling these GHG emissions in order to propose optimal FRBS designs that maximize their environmental benefits. One such factor is the spread of non-native earthworms, whose burrowing activities may modify soil emission rates of CO2, N2O and CH4. To test the effects of earthworms on GHG emissions, microcosm studies were conducted using a replicated factorial design comprising of three soil origins (deciduous FRBS, coniferous FRBS, agricultural field) × two soil textures (field conditions, high clay) × three EW life habits (anecic, endogeic, no earthworms). At different intervals over the course of a 10-week trial, we measured net CO2 emissions under aerobic conditions, as well as potential N2O emissions in microcosms amended with acetylene gas. In a separate trial using the same experimental design, we measured gross production and consumption rates of CH4, in both aerobic and anaerobic conditions, using an 13CH4 isotope dilution technique. Anecic earthworms had a positive effect on soil CO2 and denitrification, which decreased after a few weeks. Increasing soil clay content had a negative effect on the emission of these two GHGs. Additionally, soils from FRBS emitted more CO2, N2O and CH4 than soils from agricultural fields. Gross CH4 consumption rates were greater under aerobic than aerobic conditions, especially under deciduous trees. Results suggest that the inclusion of trees in riparian buffer strips combined with the introduction of non-native earthworm species could substantially increase GHG emissions of agroecosystems and mitigate the environmental benefits of FRBS.(Note: The first and second authors contributed equally to this presentation).
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".