Bibliographic record
Abstract
This article reports on the BGA/Ground Board presentation held at the Institution of Civil Engineers (ICE) on 9 February 2000. J Coggan discussed the findings from two sites in Cornwall, England: Delabole Quarry and a china clay pit. D Stead spoke mainly about the Frank Slide in Alberta, Canada, and also commented on examples of landslides in Europe. The themes of the discussions was ultimately how mathematical modelling could help engineers to understand failure mechanisms, and primarily involved back analyses of existing failures rather than prediction. Both presentations discussed the understanding of geological controls in the landslide, identification of failure mechanisms, monitoring of displacements, limit equilibrium analyses, and numerical modelling. The possible future of landslide modelling was summarised as: (1) improved modelling of failure mechanisms, such as particle flow codes able to simulate the propagation of fractures; (2) integrated modelling and risk assessment; (3) going from two-dimensional to three-dimensional modelling, which has increased data demands and improved modelling constraints; (4) application of coupled FEM/DEM codes; and (5) the use of parallel computers in slope analysis, to include more detail and run much larger jobs.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".