Inclinometer Data Analysis for Remediated Landslides
Bibliographic record
Abstract
Landslides are frequently remediated by constructing engineered fills, improving drainage, or other more specialized construction methods. Monitoring instruments, such as surface monuments and inclinometers, are sometimes installed to evaluate the performance of the remedial measures. Where remediation involves engineered fills, the engineer should recognize that compacted fills undergo an equilibration process that can take years. This process can involve heave caused by expansive soil and consolidation due to the weight of the fill and imposed structural loads. Additionally, fills placed upon hillsides can be subjected to differential settlement consistent with fill thickness and development changes, surface creep, and lateral extension. Monitoring data can indicate various subsurface movements which are a product of settlement of the fill mass and lateral extension, and not related to movement of the remediated landslide. Some of these conditions were encountered at a site in Northern California. Misinterpretation of the gathered data could easily have occurred, possibly initiating unwarranted remedial measures or preventing development. However, the use of certain analytical and data presentation methods clearly showed that the remediated landslide was performing as designed.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".