Using Eliminative Argumentation to Enhance Trust in ILI Results
Bibliographic record
Abstract
Abstract Pipeline operators have traditionally relied on unity plots from integrity digs and their confidence in the in-line inspection (ILI) tool vendor as a basis for trust in the results of ILI. However, past digs provide a narrow view of ILI success, and operators have limited visibility into the vendor’s equipment and processes. In this paper, we describe an analytical approach for the pipeline operator and the tool vendor to collaboratively enhance trust in ILI results. Borrowing methods from safety assurance decision-making in the automotive, rail and nuclear power industries, we present a live and reuseable assurance case framework in eliminative argumentation (EA) produced following this approach. This approach covers all factors impacting inspection results, from identifying required inspection performance to equipment and processes used by the vendor. Safety performance indicators derived from the assurance case can be used as warning signs that adverse events might have occurred during the inspection and that an ILI run might require further examination to confirm the trustworthiness of the results. We also describe our experience applying this methodology to create an assurance case for an actual ILI program. Our experience demonstrated the benefits of involving the vendor directly in constructing the assurance case. The structure of the assurance case clearly defines the causal connection or “golden thread” between the evidence (including indicators) and trust in the inspection. This traceability allows the operator to differentiate between minor deviations from the norm that do not impact the trustworthiness of the ILI results, and anomalies that are of greater concern. Overall, this approach yields a comprehensive, robust, and examinable basis for trust in ILI results while reducing reliance on integrity digs.
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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 | 0.072 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".