Multi‐Domain Reactive Transport Modeling of GHG Emissions From Macroporous Agricultural Soils With a Focus on N <sub>2</sub> O Hotspots and Hot Moments
Bibliographic record
Abstract
Abstract Agricultural soils significantly contribute to greenhouse gas (GHG) emissions, influencing global climate change through natural microbial processes and human activities. Soil structure plays a crucial role in regulating these processes, especially in driving the formation of GHG hotspots and hot moments. We developed a new multi‐domain reactive transport model to assess the mechanisms governing GHG emissions from macroporous agricultural soils, focusing on N 2 O hotspots and hot moments. The multi‐domain model adequately reproduced observed soil moisture, pore gas O 2 and GHG levels, and short‐ and long‐term fluxes at an experimental site in eastern Ontario, Canada, outperforming other modeling approaches (i.e., uniform and dual porosity/permeability models). Building on the calibrated model, we focused on investigating the development of N 2 O hotspot and hot moments. While consistent with conventional assumptions that N 2 O production is concentrated in organic soils and subsoils, our results further reveal the fine‐scale spatiotemporal variability of hotspots driven by localized variations in moisture, anaerobic conditions, and nutrient and organic carbon availability. N 2 O hot moments occurring during periods of heavy rainfall were closely linked with N 2 O hotspots in organic soils. A combination of sensitivity analysis and structural equation modeling suggests that the formation of N 2 O hotspots and hot moments is strongly influenced by physical and geochemical parameters, such as immobile matrix radius, gas saturation within the mobile matrix, and the ratio of N 2 O production to consumption. While aligning with field data, the model's complexity poses challenges, including non‐uniqueness due to uncertain inputs, subdomain characterization, and gas/solute transfer between the subdomains. Nevertheless, the multi‐domain model provides valuable insights that can guide future research into how complex soil structural characteristics influence GHG cycling and emissions from agricultural soils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".