Modified hydraulic conductivity equations of bentonite-based materials under saturated and unsaturated conditions
Bibliographic record
Abstract
Determining the hydraulic conductivity of bentonite-based materials holds paramount importance in the design of deep geological repositories for the radioactive waste disposal. Due to the substantial presence of strongly adsorbed water in bentonite-based materials, existing models often fail to accurately calculate their hydraulic conductivities. This study first proposed a method to quantify the volume of both capillary and adsorptive water within bentonite-based materials based on the crystallographic information of montmorillonite mineral. Then, calculating the hydraulic conductivities for both saturated and unsaturated bentonite-based materials is improved by considering the different roles of capillary and adsorptive water. At saturated conditions, capillary water dominates the water flow, and the Kozeny–Carman equation was modified to calculate the saturated hydraulic conductivity by subtracting the surface area and void ratio of adsorptive water pores. For unsaturated conditions, a new water retention model was developed. It is represented by a piecewise and continuous function that distinguishes between capillarity-dominated and adsorption-dominated processes. This water retention model is analytically integrable to be used with the Mualem model for calculating the relative hydraulic conductivity. Verifications show that the proposed equations can successfully determine the hydraulic conductivities of bentonite-based materials under saturated and unsaturated conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".