Development of LaserPro System for External Corrosion Mapping With Integrated Assessment
Bibliographic record
Abstract
This paper presents the design and application of a new semi-automatic tool for mapping external pipeline corrosion. The device hardware is complemented by the implementation of current corrosion assessment techniques via online software. The development of this device is based on over 10 years of experience in external corrosion mapping and automated scanner development, in support of an experimental research program at the University of Waterloo. The mapping device hardware consists of a novel position measurement system which is coupled to a laser-based depth measurement device to generate a surface map of corrosion defects. Direct comparison with manual (pit gauge) and fully automated measurements has shown that a given defect can be quickly and accurately mapped using the new system. Automated data capture and evaluation has demonstrated significant improvements over fully manual pit gauge-based methods. In addition, the manual position of the measurement head allows the user to map only those areas which are pertinent to the assessment. This is in contrast to fully automated methods which require continuous measurements over predefined areas of a pipe. Online feedback compliments this system and allows the operator to determine if the data set is complete by providing information on the convergence of the predicted failure pressure. Using this new mapping device, measured surface maps of natural complex corrosion defects are shown to compare favorably with other mapping devices and manually measured defect dimensions.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".