In-Line Inspection Performance III: Effect of In-Ditch Errors in Determining ILI Performance
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
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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