Influence of landscape position and climatic seasonality on soil water and gas conductivity properties in agricultural soils
Bibliographic record
Abstract
Agricultural landscape management and climate seasonality can influence soil structure, hydraulic conductivity, and air permeability within the context of soil water and soil gas mobility. To investigate this, in situ and laboratory-based data were collected from three agricultural landscape positions within a watershed in eastern Ontario, Canada during a growing season. Macropore classification, water infiltration tests, and air permeability measurements were conducted in situ and standard soil characterizations were carried out on soil samples. Hydraulic conductivity of the soil matrix, based on grain size data, indicated that the highest values were consistently measured in the B horizon at each landscape setting. Macropores were found to be more abundant within uncultivated drainage ditch bank soils, compared to the adjacent cropped fields. Macropores in the ditch bank soils were exclusively consisted of circular biopores, while both circular and linear macropores were observed in the cultivated field soils. Air permeability, vertical hydraulic conductivity, and horizontal hydraulic conductivity were also greater in the uncultivated soils, relative to the cultivated soils. Field saturated hydraulic conductivity measurements offered evidence of anisotropy, likely due to the vertical nature of the macropore features. Macropore disposition and extent varied over the growing season, especially in the cultivated field soils where tillage and field trafficking are physically disruptive. Seasonality of macropore development will influence temporal changes in advection-based mass exchange of gas and water in the vadose zone. Modeling of mass exchange in agricultural soils should consider time variability in macroporosity to more realistically characterize infiltration and soil gas emissions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".