Advanced Non-Destructive Methods for Defect Characterization Under Coating for In-Service Storage Tanks
Bibliographic record
Abstract
Abstract In recent years there has been significant development of non-destructive technologies for on-stream inspection using remotely operated tools. Many operator companies have a keen interest in adopting such technologies to fulfil the integrity, reliability, and regulatory requirements while minimizing the operational impact. There has been substantial development of in-line inspection tools for pipeline defect characterization, but there is an industry gap of such tools for in-service floor inspections of crude oil storage tanks. Further research and development are required to overcome the challenges of sludge removal, sensor data acquisition under sediments, tool navigation in a viscous product, and electrical hazards in flammable and combustible products. To accelerate industry innovation, Enbridge has designed and constructed a test tank environment which is a small-scale version of a large-capacity crude storage tank with prefabricated floor defects for vendors to evaluate their robotic in-service inspection equipment. This paper will describe the test tank design as well as the stages of the project for evaluating tool performance in different product environments. In the first stage, the tools will be tested in water, and their performance will be compared with conventional technologies used in out-of-service inspections.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".