Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Conventional slope practice, based on the deterministic factor of safety, cannot address the uncertainty in the input parameters of slope analyses in any explicit way. It relies entirely on the subjective judgment of the designer, which varies substantially among geotechnical engineers. Probabilistic techniques are powerful tools that can be used to quantify and incorporate uncertainty into slope analysis and design. A probabilistic slope analysis methodology based on Monte Carlo simulation using Microsoft® Excel and @Risk software is applied to investigate the Lodalen slide that occurred in Norway in 1954. Starting with field and laboratory data, the study demonstrates the techniques used in quantifying the uncertainties in soil properties and pore-water pressure, conducting a probabilistic assessment, and estimating the probability of unsatisfactory performance. The probability of unsatisfactory performance of the Lodalen slope is estimated to be 0.70, indicating that failure was imminent. The inclination of the Lodalen slope is then flattened, hypothetically, to different angles and the relationships between the slope angle, the factor of safety, and the probability of unsatisfactory performance are investigated.Key words: probabilistic analysis, slope stability, Monte Carlo simulation, spatial variability, Lodalen slide.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it