Second ISSMGE R. Kerry Rowe Lecture: On the intrinsic, state, and fabric parameters of active clays for contaminant control
Bibliographic record
Abstract
The osmotic, hydraulic and self-healing efficiency of bentonite-based barriers for the containment of subsoil pollutants is governed not only by the intrinsic chemicophysical parameters of the bentonite, i.e., the solid phase density, ρ sk ; the total specific surface, S; the surface density of the electric charge, σ; and the Stern layer thickness, d Stern , and fraction, f Stern , but also by the chemicomechanical fabric parameters that quantify the structure or texture of the solid skeleton, such as the micro, e m , and nano, e n , void ratios; the average number of platelets or lamellae per tactoid, N l,AV ; and the solid skeleton effective electric charge concentration, [Formula: see text]. In turn, the fabric parameters are controlled by state parameters, such as the total void ratio, e; and the salt concentration of the equilibrium solution, c s . A theoretical framework has been developed to describe the relationships between the aforementioned intrinsic, state, and fabric parameters for a bentonite barrier and its performance parameters: the hydraulic conductivity, k; the effective diffusion coefficient, [Formula: see text]; the chemico-osmotic efficiency coefficient, ω; and the osmotic swelling pressure, u sw . The proposed theoretical hydrochemicomechanical model has been validated through comparison with a large amount of experimental results.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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".