Bibliographic record
Abstract
Mixed fluid phenomena in porous media have profound implications on soil-atmosphere interaction, energy geotechnology, environmental engineering and infrastructure design. Surface tension varies with pressure, temperature, solute concentration, and surfactant concentration; on the other hand, the contact angle responds to interfacial tensions, surface topography, invasion velocity, and chemical interactions. Interfaces are not isolated but interact through the fluid pressure and respond to external fields. Jumps, snap-offs and percolating wetting liquids along edges and crevices are ubiquitous in real, non-cylindrical porous networks. Pore- and macroscale instabilities together with pore structure variability-and-correlation favor fluid trapping and hinder recovery efficiency. The saturation-pressure characteristic curve is affected by the saturation-history, flow-rate, the mechanical response of the porous medium, and time-dependent reactive and diffusive processes; in addition, there are salient differences between unsaturation by internal gas nucleation and gas invasion. Capillary forces add to other skeletal forces in the porous medium and can generate open-mode discontinuities when the capillary entry pressure is high relative to the effective stress. Time emerges as an important variable in mixed-fluid conditions and common quasi-static analyses may fail to capture the system response.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".