Programmatic Approaches to Assessing and Mitigating Risk to Pipelines from Geohazards
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
Many gas and liquid pipelines traverse unstable landscapes, such as waterways, floodplains, and steep terrain. These unstable landscapes often pose threats to pipeline stability and integrity. A variety of approaches are available to better understand and predict the magnitude and frequency of natural forces (geohazards) that threaten pipeline integrity. Risks of pipeline failures from these threats are of high concern to the industry, and the risks escalate significantly in high consequence areas. One approach is to prioritize vulnerable pipelines by assessing and prioritizing them through a combination of publicly available data, operator knowledge, and site-specific information (e.g., pipeline characteristics, recent survey data, etc.). The objective is for hydrologists, geologists, engineers, and environmental scientists to evaluate potential failure risk for those pipelines for which a refined understanding of vulnerability is desired. Depending on the results/outputs of a pipeline natural forces assessment, it may then be critical to understand requirements to permit, design, and implement recommended actions to minimize potential risk at pipeline segments of concern—through monitoring plans, operational controls, and/or engineering controls.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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