Gas Dispersion and Immobile Gas Content in Granular Porous Media
Bibliographic record
Abstract
Gas dispersion is an important process often controlling gas transport in porous media. At present, however, relatively little is known about the relationship between gas dispersion and porous media physical properties. In this study, gas dispersion and mobile gas content in porous media were measured as a function of medium average particle diameter, particle size range, and pore gas velocity. A set of natural granular media consisting of sand and gravel with uniform particle size distributions covering average particle diameters from 0.5 to 14 mm was used. Clean sand and gravel were used to obtain media with specific particle size distributions via mechanical sieving. Gas dispersion and mobile gas content were measured by column tracer experiments using atmospheric air and nitrogen as tracer gases. Gas dispersion coefficients, gas dispersivities, and mobile gas contents were determined by fitting the advection-dispersion equation to the measured gas breakthrough curves. The results showed that gas dispersivity decreased with increasing mean particle diameter and increased with increasing particle size range (width of particle size distribution). Thus, the largest dispersivities were observed for media with a small mean particle diameter and a wide particle size distribution. A model concept for predicting gas dispersivity from mean particle diameter and particle size range was proposed. The mobile gas content increased with increasing pore gas velocity but was independent of either particle size range or mean particle diameter.
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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 | 0.000 |
| 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".