ILI Performance Verification and Assessment Using Statistical Hypothesis Testing
Bibliographic record
Abstract
Following an inspection, API 1163 recommends that operators verify the accuracy of the ILI measurements. This paper examines the performance of an ILI tool from two separate perspectives. The first question is whether the ILI tool’s performance meets expectations and contractual requirements. The second question is to what level of accuracy the ILI tool can be relied upon for making integrity related decisions. This paper develops a method of determining the number of excavations and the number of anomalies to be investigated in the field to verify and assess the accuracy of the ILI reported depths. The verification and assessment of the ILI accuracy are two separate questions, and each is addressed as a hypothesis testing procedure. The first hypothesis states that the tool meets expected and contractual standards. That hypothesis is tested against the excavation data. Its acceptance means that the excavation data is consistent with the expected accuracy of the tool, but it does not specifically verify that accuracy. A second hypothesis states that the tool fails to meet some level accuracy as stated by a tolerance and certainty level. That hypothesis is constructed so that when it is tested against the excavation data it is rejected. Its rejection means that the tool exceeds the stated level of accuracy with a high degree of confidence.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".