Spatiotemporal Analysis of the Relative Soil Gas Diffusion Coefficient in Two Sandy Soils: Variability Decomposition and Correlations between Sampling Dates at Two Spatial Scales
Bibliographic record
Abstract
The variability of the relative soil gas diffusion coefficient (Ds/Do) in space and time is not well known but is important for root respiration, microbial activities, and greenhouse gas emissions. The objectives of this study were to: (i) quantify the spatial variability of Ds/Do; (ii) decompose this variability into components at small vs. large scales relative to the size of the site; and (iii) analyze temporal changes at each spatial scale during the growing season of 2006 and the spring of 2007 at two depths in two sandy soils under potato (Solanum tuberosum L.)–corn (Zea mays L.) rotation. The coefficient of variation of predicted Ds/Do varied from 30% at 0.15-m depth to 101% at 0.30-m depth, possibly because of a change in soil horizons around the 0.30-m depth. The variability in the data on each sampling date was decomposed into small-scale (spatial and nonspatial) and large-scale (spatial) components using the coregionalization analysis with a drift method. Overall, the small-scale component was predominant at both sites, especially at the 0.15-m depth. Spatial structures were maintained during 2006 and partially carried over to the next year, particularly at the 0.30-m depth, except for the large-scale spatial distribution patterns observed at both depths at Site 1. These results indicate that variability between years may be higher than within a year, probably due to tillage, plant growth, and snowmelt. The study suggests that spatiotemporal variability of Ds/Do should be considered in agricultural research and precision farming approaches. Longer term studies would increase understanding of temporal trends in the spatial distribution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".