How groundwater seepage and transport modelling software can be useful for studying gaseous transport in an unsaturated soil
Bibliographic record
Abstract
Abstract The aim of this paper is to show how standard hydrogeologic software, usually used to model contaminant transport in groundwater under unsaturated conditions, can also be used to model gas transport in unsaturated porous media. Physical processes involved in the interaction between the atmosphere and the unsaturated soils are considered: transport by diffusion through the air and the groundwater, exchange between the liquid and gas phases and consumption. These physical processes are incorporated into the governing equations of a groundwater numerical code; by considering air, contained in the unsaturated soil, as water in the seepage numerical model, the air effectively becomes fluid within the numerical code. Then, the investigated gas is defined as the contaminant in the transport model, which is transported by ‐the air for the modeller‐, and ‐water for the numerical code‐. The over‐riding assumption is that the air profiles and, therefore, water profiles of volume contents remain constant. The approach is illustrated using two examples, which consider the transport of oxygen. The first deals with oxygen distribution through a laboratory‐cell diffusion containing reactive mining tailings. The second deals with the oxygen fluxes through the vadose zone, between the atmosphere and an unconfined aquifer's water table. Both examples consider different cases of oxygen consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".