Probability risk assessment of landslides: A case study at Finneidfjord
Bibliographic record
Abstract
Probabilistic risk assessments are increasingly being considered the most appropriate framework for engineers to systematically base decisions on hazard mitigation issues. This paper aims to show the advantages of a quantitative risk assessment by application to a historical case study. The generalized integrated risk assessment framework has been applied retrospectively to a submarine landslide that occurred in 1996 near the village of Finneidfjord in northern Norway. Over 1 million cubic metres of predominantly quick clay was displaced. Even though it was triggered underwater on the embankment of the Sørfjord, the retrogressive nature of the slide resulted in it encroaching 100–150 m inland. The triggering mechanism is believed to have been the placement of fill, from a nearby tunnelling project, on the foreshore of the embankment. This paper is a retrospective quantitative evaluation of the risk to the neighbouring houses, the persons in those houses, and the persons in open spaces caused by the placement of increasing levels of embankment fill. A probabilistic approach, making use of second-moment modelling and first-order second-moment approximation is adopted. It aims to demonstrate the advantages of this type of risk assessment in understanding complex and integrated hazards, particularly those in populated environments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".