Detection of Active Corrosion From a Comparison of ILI Runs
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
Repeated in-line inspections (ILI) of transmission pipelines have been used for many years to estimate corrosion rates. However, the calculation of a corrosion rate from a direct comparison of ILI anomalies is often dominated by the ILI measurement error. As an alternative to assessing a corrosion rate, it may be possible to use repeated in-line inspections to simply detect the presence of active corrosion. This paper presents the application of various statistical measures to detect active corrosion with a high-level of confidence. From a pipeline integrity management perspective, this method will enable the operator to address each location where there is a high probability of active corrosion. Furthermore, despite there being no explicit calculation of corrosion rates, the advantage of the method is that it can yield an upper bound on the corrosion rate of anomalies left unexcavated on the pipeline.
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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) | 0.001 | 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 it