Inclinometer Data Analysis for Remediated Landslides
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
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.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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