Effects of Freeze–Thaw and Soil Structure on Nitrous Oxide Produced in a Clay Soil
Bibliographic record
Abstract
Freezing and thawing have been shown to cause significant soil physical and biological changes. The increase in denitrification following thawing may be attributed to the diffusion of organic substrates newly available to denitrifiers from disrupted soil aggregates. The objective of this study was to evaluate the effect of freezing and thawing on N 2 O production in a clay soil under contrasting crop rotations and tillage practices. Laboratory experiments were conducted in soil slurries to favor substrate diffusion, in macroaggregate fractions separated by wet sieving to characterize the biologically active soil organic matter (SOM) pool, and in undisturbed soil cores to simulate field conditions. In slurries, a freezing and thawing cycle increased denitrification rates by 32%. Soil slurries from no‐tillage under rotation (NT–R) exhibited denitrification rates 92% higher than those from conventional till under continuous cereal (CT–C). Macroaggregates fractions (0.25–2 and 2–5 mm) from both management systems increased their rates of C mineralization and denitrification activity by 95% following freezing, but the increases tended to be greater (57%) in small than in large macroaggregates. Higher rates of denitrification (55%) found in both aggregate fractions of NT–R system were attributed to the higher mineralizable organic C content. Undisturbed soil cores sampled in November showed increased N 2 O production by 220% after thawing. This thawing effect was also significantly higher in cores from NT–R than in those from CT–C.
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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) | 0.000 | 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".