Performance Estimation of a Remote Field Eddy Current Method for the Inspection of Water Distribution Pipes
Bibliographic record
Abstract
Remote Field Eddy Current (RFEC) technology allows the in situ inspection of metallic water distribution pipes. RFEC tools provide the location and magnitude of corrosion defects on the inspected pipes. The capacity of an RFEC tool to detect corrosion defects is evaluated in this paper by comparing its results with those obtained from the analysis of computed tomography (CT) scan images of the inspected pipes. Localization and characteristics of defects identified with the RFEC tool and from the CT scan images were compared for six cast iron pipes. An original method is proposed for the analysis of the CT scan images from which wall thickness losses were estimated by using the basic principle that the attenuation coefficient of X-rays in a homogenous material is a linear function of its density. The results show that the RFEC tool is able to localize most of the defects identified from the analysis of CT scan images. These findings reveal that the tested RFEC probe provided reliable information on the main corrosion defects, and thus on the general structural integrity of the inspected pipes.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".