A Combined Approach to Characterization of Dent With Metal Loss
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Current in-line inspection technologies (e.g., Caliper/MFL or Combo) for mechanical damage characterization can detect dent with metal loss but with limited ability to discriminate metal loss between corrosion, gouge and crack with certainty. There are also some cases that metal loss signals were detected but not reported by ILI vendors because of either signals below threshold for reporting or other reasons. Practical experience showed that, with assistance of strain based dent analysis and strain limit damage criteria; detailed characterization of MFL tri-axial signals could effectively facilitate to discriminate metal loss features and identify potential risk of cracks or gouges in the dent. In this paper, the newly developed approach is utilized to identify the critical dents in the pipelines and discriminate those dents associated with metal loss reported by combined ILI technologies. A case study was performed with four real dent features, as an example to demonstrate the effectiveness of this approach. The details of the case study, results and findings are summarized in this paper.
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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 | 0.000 |
| 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 it