Pipeline Integrity Evaluation of Oil Pipelines Using Free-Swimming Acoustic Technology
Bibliographic record
Abstract
Significant financial and environmental consequences often result from line leakage of oil product pipelines. Product can escape into the surrounding soil as even the smallest leak can lead to rupture of the pipeline. From a health perspective, water supplies may be tainted by oil migrating into aquifers. 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 tool called SmartBall®, which is capable of detecting leaks as small as 0.03 gpm in oil product pipelines. The tool swims through the pipeline being assessed and produces results at significantly reduced cost to the end user compared to current leak detection methods. GPS synchronized GIS-based above ground loggers capture low frequency acoustic signatures and digitally log the passage of the tool through a pipeline. This paper presents the development, laboratory and field validation testing of the SmartBall for oil pipeline integrity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".