Development of an Innovative Free-Swimming Device for Detection of Leaks in Oil and Gas Pipelines
Bibliographic record
Abstract
Line leakage of oil and natural gas systems can result in significant financial and environmental consequences. Often, small leaks lead to ruptures in the pipeline that result in product escaping into the surrounding soil. Oil product can taint water supplies by migrating into aquifers, while natural gas leaks are susceptible to catastrophic explosions. A joint academic-industry research initiative funded by the U.S. Department of Transportation's Pipeline and Hazardous Materials Safety Administration (PHMSA) has lead to the development and refinement of a free-swimming device capable of detecting small leaks in oil product and natural gas pipelines. The SmartBall swims through a pipeline being assessed and produces results at significantly reduced cost to the end user compared to current leak detection methods. GIS based above ground loggers that are GPS synchronized capture low frequency acoustic signatures and digitally log the passage of the SmartBall through a pipeline. This paper presents the development, laboratory and field validation testing of the SmartBall for both oil and natural gas applications with discussion of demonstrated case studies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".