Influence of preparation techniques on the index properties of clay
Bibliographic record
Abstract
Liquid limit (LL) and plastic limit (PL) are frequently used as indicators of the behaviour of fine-grained materials. These parameters are a prerequisite in every geotechnical investigation. Careful and consistent material preparation is a key component in the laboratory procedures adopted to determine the LL and PL. In preparing samples for such tests, various techniques are used in practice, and this paper examines the influence of these techniques on the actual magnitude of LL and PL measured. The index properties of a glaciolacustrine deposit, locally known as Belfast Upper Boulder Clay, which was laid down in glacial Lake Belfast, are examined. The index properties were determined on samples of the material prepared in different ways, including drying the material at two different temperatures (40°C and 110°C), crushing the dried material from coarse granular to fine granular form by adopting varying degrees of crushing effort, and mixing the dry material with both deionised water and tap water. The results indicate that the crushing procedure has a significant influence on particle size distribution and a consequential effect on the LL and PL. Pore water chemistry and the drying procedure also have a measurable influence on the magnitude of LL and PL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".