Bibliographic record
Abstract
This paper describes the hardware and software of a laser scanning system that is used to produce 3D images of external surfaces of pipes. The images are produced in the form of 3D raster images with a resolution of up to 0.1×0.1 mm and an accuracy of 3-10 microns, depending on the desired scanning speed, to portray the corrosion pits on the pipes. The main application of the scanner is to establish patterns for calibrating nondestructive testing techniques (e.g., Remote Field Eddy Current (RFEC) and ultrasound testing that are commonly used to measure the remaining wall thickness of ductile and cast iron pipes), and also identifying the statistical characteristics of the measuring instruments utilized in those methods. The images may also be useful to scrutinize corrosion and failure mechanisms, especially when estimates of average or maximum pit depth are insufficient. The scanning system consists of a 2-DOF robot that can move a laser displacement sensor along a pipe and an instrumented rig that rotates the pipe about its axis. Rotating the pipe and moving the rangefinder along the pipe’s axis, the scanning system acquires laser measurements into a host computer to produce the image. The paper also presents the images and statistical analysis of corrosion pits of pipe samples exhumed and sandblasted for scanning.
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 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 | 0.000 | 0.001 |
| 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".