Methods used to determine maximum and minimum dry unit weights of sand: Is there a need for a new standard?
Bibliographic record
Abstract
Known challenges exist with maximum (γdmax) and minimum (γdmin) dry unit weight measurements; the respective dry unit weight results depend very much on the method or standard used. A laboratory testing programme was completed to systematically determine and compare γdmax and γdmin values derived for six different sand types by using different methods. The tested sands contained a wide variety of mineralogical and fines contents. The γdmax and γdmin determinations were performed according to the following methods: British Standards Institution (BS) standards; American Society for Testing and Materials (ASTM) standards; Deutsches Institut für Normung (DIN) standards; Dansk Geoteknisk Forening (DGF) guidelines; Norwegian Geotechnical Institute (NGI), Geolabs, and Fugro proprietary methods. Differences in testing procedures, material requirements for testing, and the effects of soil degradation during testing introduce challenges and large differences in γdmax and γdmin values for each of the six sand types were observed. Therefore, it is concluded that there is a need for the development of new standards for a robust determination of γdmax and γdmin values. Specifically, a standard for determining γdmax is required to consistently obtain results at the upper bound of dry unit weight values for the likely range of sands — without crushing the sand grains.
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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 | 0.037 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".