Influence of Cover Crops and Winter Warming on Soil N<sub>2</sub>O Content and Surface Fluxes During Freeze‐Thaw
Bibliographic record
Abstract
Abstract Overwinter cover crops alter nitrogen dynamics and soil temperatures, potentially mitigating nitrous oxide (N 2 O) emissions during freeze‐thaw (FT). Meanwhile, increasing winter temperatures can remove the insulating snow layer intensifying FT cycles and N 2 O fluxes. Comparing the patterns of soil N 2 O content against surface fluxes under different management and environmental conditions can improve the understanding of what mechanisms enhance N 2 O fluxes at thaw. Soil profile (0–140 cm) N 2 O gas concentrations and fluxes were measured from December to April in large‐scale lysimeters with two dominant soils in Ontario, Canada (silt loam and loamy sand) over two years. The simultaneous heat and water model was used to simulate liquid water and ice content during freezing conditions, needed for total N 2 O content estimations (i.e., aqueous + gaseous). During year 1, the peak soil N 2 O content ranged from 23.6 to 79.0 mg N 2 O m −2 , and two significant emissions events occurred (9.6–41 g N 2 O‐N ha −1 d −1 ). In year 2, no significant N 2 O profile accumulation or emissions were observed due to warm winter conditions. Difference in soil physical conditions impacted the response of soil N 2 O content to cover crops, with N 2 O content decreasing by 42% in the loam soil and increasing 101% in sand. Intermittent heating caused colder soil conditions in year 1, increasing soil N 2 O content in loam soil while reducing it in sand. Despite changes in soil N 2 O content, the N 2 O surface flux was not impacted, indicating that alternative nitrogen loss pathways are likely responsible for reducing N 2 O content during FT events not surface fluxes.
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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 | 0.000 | 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".