<i>The magic of sands</i>— The 20th Bjerrum Lecture presented in Oslo, 25 November 2005This paper represents the written version of the 20th Bjerrum Lecture. While it has been edited for the present publication, it retains the general structure of the original lecture, which was intended for a general geotechnical audience. It deliberately points out some of the still-open questions concerning soil modelling. The Bjerrum Lecture is presented in Oslo in alternate years by the Norwegian Geotechnical Society with the support of the Bjerrum Memorial Fund (Laurits Bjerrums Minnefond).
Bibliographic record
Abstract
Following the discovery of sinkholes in the W.A.C. Bennett Dam, British Columbia, in 1996, investigations showed that there had apparently been movement of fine material out of the core of the dam. To be able to predict the mechanical consequences of such movement of material, a class of soil model that is able to accommodate changes in both density and grading of the soil is required. An outline of features of the Severn–Trent sand model — which incorporates effects of density variation — is presented and a suggestion is made for ways this model might be extended to include effects of changing particle size distribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.019 |
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".