Physical observations of the transient evolution of the porosity distribution during internal erosion using spatial time domain reflectometry
Bibliographic record
Abstract
A purpose-built permeameter was used to explore the transient evolution of porosity during the mixing process in filtration experiments. The experiments considered upward seepage flow and explored the influence of base and filter particle sizes, along with different hydraulic conditions. The permeameter acted as a coaxial transmission line enabling electromagnetic measurements based on spatial time domain reflectometry, from which the porosity profile was obtained using an inversion technique. Quantitative characteristics of the onset and progression of the mixing process were extracted from a porosity field map. The limiting onset condition was influenced by geometric and hydraulic factors, with the critical flow rate exhibiting a strong dependence on the base particle size, while the critical hydraulic gradient exhibited a stronger dependence on filter particle size. The progression of the mixing process was characterised by both the transport of base particles into the filter layer, as well as the settlement of the filter particles into the base layer due to the reduction of the effective stress at the base–filter interface leading to partial bearing failure. The rate of development of the mixture zone was strongly dependent on the hydraulic loading condition and the base particle size, but the final height of the sample after complete mixing was independent of the hydraulic loading path.
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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".