Using Eliminative Argumentation to Enhance Trust in ILI Results
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
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.
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 | 0.000 | 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 it