Soil‐gas diffusivity in large soil monoliths
Bibliographic record
Abstract
Summary The variability of gas diffusion in soil is not well known, but is important for assessing greenhouse gas emissions, soil decontamination, oxidation in soil and plant and root respiration. The goal of this study was to assess small‐scale variability of the relative soil‐gas diffusivity ( D s / D o , m soil air ) using large intact soil monoliths and to compare D s / D o calculation methods. Neon (Ne) was maintained constant at the lower boundary of three monoliths of two soils (a sand and an organic soil). Ne concentration was measured at large spatial and temporal frequencies. Calculation methods included the use of average concentration, and average D s / D o per horizon, per section, or for the entire soil profile. Considering all sections of the monoliths, D s / D o varied from 3.5 × 10 −3 to 1.2 × 10 −1 for the A p horizon and from 4.8 × 10 −3 to 8.3 × 10 −1 for the B f horizon in the sand and from 1.0 × 10 −3 to 7.9 × 10 −3 for the O hp horizon and from 2.4 × 10 −4 to 7.7 × 10 −2 for the O f horizon in the organic soil. For the entire soil profile, variations in D s / D o between monoliths reached 125% in the sand and 56% in the organic soil. The D s / D o calculation method influenced the apparent variability (CV) of D s / D o and, to a lesser extent, D s / D o values of the overall soil profile. Differences in D s / D o between monoliths could not be explained solely by the variability of total soil porosity and air‐filled porosity. Soil macroporosity (cracks and earthworm burrows) and layering greatly influenced variability of gas movement. Thus, the choice of sampling procedure, calculation method and modelling must be governed by the scale of the processes of interest and soil variability attributes.
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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".