Leakage Prevention and Real-Time Internal Detection in Pipelines Using a Built-In Wireless Information and Communication Network
Bibliographic record
Abstract
Summary A series of recent pipeline leakage incidents created severe societal concerns to a point of impeding, or even completely preventing, building new pipelines in North America. Various systems have been proposed to identify and locate leakages. However, despite the fact that pipelines remain the safest means of oil and gas transportation, incidents still persist and pipeline acceptance from the public has become compromised. In order to address the need for early leakage detection, while providing comprehensive leakage prevention, a novel pipeline system is proposed. This concept builds on the already existing pipe-in-pipe design by segmenting the pipeline system with segmentation rings and embedding a linear wireless network in the annular airgap between the two pipe layers. Presence of fluid in the case of a leakage into the interpipe space causes degradation of the wireless network to a point of interrupting the communication in a particular pipeline segment well before any external leak occurs. Thus, the internal leak is detected in real time by an external central unit connected to the linear wireless network, as demonstrated with a 6 ft 8 in. experimental pipeline setup.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".