The evolution of offshore survey technology for pipeline inspections
Bibliographic record
Abstract
The offshore survey industry continues to develop and introduce new technologies. Significant advancements in a variety of technologies have led to the successful introduction of AUV's for a variety of surveying roles. However their impact on the “high end” pipeline inspection is not yet complete. Can AUV's become the standard acquisition platform for pipeline and other inspection surveys or are the challenges and obstacles that prevent the uptake and adoption of AUV technology simply too great? There are now component technologies that, when properly constructed, enable the use of AUV technology for many more survey and inspection related activities. This includes the collection, processing and management of the survey sensor data sets. AUV's are now capable of carrying such sensors as the latest high resolution multibeam sonars, synthetic aperture sonars, high definition cameras and acoustic doppler current profilers. With this array of available acoustic sensors we can expect AUV's to be used for an increasing number of pipeline inspection surveys and other hydrographic survey missions. This increased usage will likely require new data processing workflows and techniques, especially in consideration of the huge data volumes that will require processing once the vehicle returns to its parent ship and data is downloaded. This paper will explore these processing workflows and highlight the challenges and benefits with a view to addressing some key questions and promoting discussion on the future use of AUVs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".