Energy Pipeline Integrity Water and Slope Crossing Assessments
Bibliographic record
Abstract
Pipelines that transport energy products exist along corridors throughout the United States and Canada. Pipeline corridors intersect fluvial systems where the pipeline is typically installed below the sediment surface to alleviate the influence (force) of flowing water on the pipeline. Pipeline corridors also traverse hills and valleys where slope movement could have a negative impact on pipeline integrity. Pipeline integrity management includes assessment of sites where conditions may subject the pipeline to adverse conditions. A method is presented whereby geomorphology and geotechnical methods were used to assess 1,124 water and slope crossing sites for 2,700 miles of pipelines. The method maximizes the use of available data from public agencies and internet sources. Geomorphic, geologic, karst, seismic, soil, hydrologic, and contour map information were used to characterize physical/environmental conditions. From this initial background assessment, sites were identified for field evaluation based on erosion/slope failure potential. Following completion of the field work the combined background and field digital data were used to assign an erosion potential rating for each water and slope crossing. The erosion potential was rated in five categories ranging from very low to very high. The erosion potential rating and field measured depth of cover were used to assign a pipeline exposure potential rating for each site. The exposure potential was similarly rated from very low to very high. The digital approach to collecting, analyzing, and reporting the data provided an effective and efficient means of evaluating large areas with complex conditions. This enables pipeline monitoring, maintenance, and capital improvements to be planned and prioritized. The digital mapping generated is a useful tool for tracking conditions over time and can be updated as conditions change in the future.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".