A Simple Relationship to Estimate Parameters of the Optimum Compaction Point
Bibliographic record
Abstract
Compacted clay covers have been the most commonly used materials for hydraulic barrier layers. During their construction, the control of some parameters such as compactive effort and molding water content is required. These last parameters affect the hydraulic conductivity, which is considered as one of the important key parameters for cover design. To reach the target in terms of hydraulic conductivity, the cover must be compacted to a pre-determined dry unit weight that usually corresponds to a certain percentage of the maximum dry unit weight (γdmax). During the prefeasibility stage of a project (before conducting the required tests), γdmax and the optimum water content (wOpt) can be estimated to obtain an early overview of the conditions that can be anticipated. In this regard, a new approach was proposed to estimate these parameters using the liquid limit (wL) and the plastic limit (wP). The proposed equations were developed using data from 56 compacted clay liners and validated using 44 others taken from published data. Results presented in this paper indicate how the proposed equations can successfully estimate parameters of the optimum compacted point.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".