Landslide initiation : a unified geostatistical and probabilistics modellin technique for terrain stability assessment
Bibliographic record
Abstract
Five open slope, translational landslides which have occurred on four separate clear-cut hillslopes in coastal British Columbia are described in terms of their qualitative, geomorphological characteristics and their hydrogeological response to extreme precipitation. The design and operation of a displacement rate controlled, large scale, in-situ direct shear box, which was used to conduct shear strength tests at three of the four locations and on laboratory-reconstituted samples, is described. A mean friction angle of 47° and a root or soil structure cohesion of 1.5 kPa is interpreted from the in-situ and laboratory testing program to be appropriate for the tested shallow, colluvial gravelly sands. Approximately eight years of near-continuous, shallow piezometric data, collected at the Carnation Creek Experimental Watershed by the Canadian Forest Service, are used to determine the piezometric response of shallow colluvial soils to extreme precipitation events and to determine an appropriate probabilistic distribution for use in Monte Carlo-like simulations of slope stability. An extreme value distribution, which is dependent on the duration of observation, is proposed for this purpose. Piezometric data and the results of infinite slope stability back-analyses indicate the potential for short-term pore pressures which are in excess of hydrostatic pressures, and potentially artesian, to develop during extreme precipitation events. Optimized terrain attribute data, collected by the British Columbia Ministry of Forests, for a subset of 1,526 mapped terrain polygons are combined with the results of Monte Carlo-like slope stability simulations of the same polygons to create a unified, geostatistical (qualitative terrain attributes) and probabilistic (quantitative slope stability) landslide initiation model. The resulting probabilities of initiation, P(In), for each polygon are compared to existing slope stability assessment techniques used in the forest sector. The proposed assessment technique is intended to represent one component of a multi-disciplinary, quantitative risk assessment approach which considers all hazards to downslope resources and the specific risk of each element at risk.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".