In-Line Inspection Performance III: Effect of In-Ditch Errors in Determining ILI Performance
Bibliographic record
Abstract
The performance of in-line inspection tools employed for pipeline integrity assessments has significant impact on the reliability of assessment results and subsequent remediation program. Previous papers by the lead authors established criteria for determining the number of “successful” field validation measurements for establishing whether a tool’s performance should be accepted or rejected and how to assess the tool performance when a claimed performance can be neither rejected nor accepted. This “apparent” validated performance contains both the ILI tool measurement error and the in-ditch measurement error. Determination of the true tool error can have significant impact on the response to ILI feature characterization relating to timing for repairs and justification of re-assessment intervals. Measurement errors, associated with the currently available technologies employed in the field for in-line inspection metal loss validation measurements, are discussed in this paper. The relative effect of variations in validated tool error due to field measurement error on pipeline integrity is discussed within the context of actual in-line assessment case studies.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".