Adsorptive transport of noble gas tracers in porous media
Bibliographic record
Abstract
The transport of noble gas radionuclides in porous media is relevant to the detection of underground nuclear detonations as well as the sequestration of reprocessing off-gases. However, in field tests releasing radioxenon underground, the quantity of radioxenon observed at the surface has fallen well below expectations.[Formula: see text] This study examined the diffusivity of noble gases (Kr and Xe) and the inert molecular gas sulfur hexafluoride (SF[Formula: see text] in porous media to observe any unexpected behavior. To replicate the transport of radiogenic noble gases in underground media, a two-bulb gaseous diffusion apparatus was constructed. The two bulbs were connected with a column of 10–30 Ottawa sand and ordinary atmosphere filled both the bulbs and pore spaces. The tracer gases were diluted in an isolated bulb to approximately 1000 ppm. Once released, the gases were allowed to diffuse through the column. Aliquots were withdrawn at regular time intervals from both bulbs and concentrations were quantified using a Shimadzu QP2010 SE gas chromatograph-mass spectrometer. The effective diffusivity was then calculated using a maximum likelihood estimate on the quasi-steady state model. The effective diffusivity of Xe in the silica sand was observed to be 135.2% that of SF[Formula: see text] whereas the effective diffusivity of Kr was observed to be 161.4% that of sulfur hexafluoride. These findings are consistent with the binary diffusivities in N[Formula: see text]: 132.6% and 161.7%, respectively. However, the apparent volume of the system was inconsistent amongst the species, with Xe converging at slightly lower gas-phase concentrations than Kr or SF[Formula: see text]. This apparent reduction in gas-phase concentration occurred within the first few measurements and is consistent with transient accumulation of an adsorbed phase. As the effective diffusivities in the silica sand were shown to be consistent with the binary diffusivities in N[Formula: see text], a porosity-tortuosity model appears to be sufficient when considering similar geological materials. However, with the observation of significant gas adsorption, consideration of adsorbed-phase accumulation is necessary when scaling to larger geological systems.
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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".