Assessing Gas Diffusion Coefficients in Growing Media from in situ Water Flow and Storage Measurements
Bibliographic record
Abstract
Knowledge of gas exchange dynamics is important in determining the suitability of growing media used in nursery and greenhouse production. However, gas diffusion measurement methods are difficult to use directly in potted growing media and a rapid, simpler, and reliable approach appears desirable for routine assessment of gas diffusivity. This study compares gas diffusivity and pore efficiency estimates from gas diffusion chamber measurement with indirect estimates obtained from water storage and flow measurements and point of air entry values for various substrates. Four peat substrates with bark in variable particle sizes, a mineral soil, and silica sand were packed into aluminum cores in four replicates and then saturated for 72 h. After equilibration at −0.8 kPa of water potential on a tension table, the concentrations of N 2 diffusing through these cores were measured in a gas diffusion chamber and gas diffusivity calculated from the gas concentration change in time. Diffusivity was also calculated with the water desorption curve and the saturated hydraulic conductivity, also measured on the same core (indirect approach). The gas diffusivity estimates obtained by gas diffusion chamber measurement were significantly correlated ( R 2 = 0.49, slope not different from 1) with those obtained from the indirect approach. Estimates obtained for pore efficiency were much closer and less variable than those for gas diffusivity ( R 2 = 0.80, slope not different from 1). The indirect approach may be a useful tool for the rapid assessment of gas exchange dynamics in growing media under undisturbed conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".