New Concepts in the Prioritization of Multiple ECDA Indications
Bibliographic record
Abstract
Abstract This paper describes the challenge of integrating specific types of ECDA indications, such as AC-enhanced corrosion (ACEC) and DC interference (DCI), under the prioritization criteria recommended by NACE RP0502-2002. Starting from the observation that the risk of corrosion does not always increase with the size of the holiday, the paper analyzes the interaction of up to four complementary ECDA indications (i.e. CIPS, DCVG, ACEC and DCI), with and without prior history of corrosion, as a function of their severity. New concepts, such as “distributed indication ” and “relevant indication ”, are introduced in order to establish the location of the direct examinations, where the indication affects entire sections of line (i.e. 10 km of line subject to severe ACEC). Simple rules are proposed for integrating these multiple ECDA indications in matrix type prioritization tables. The paper also includes an example of using these tables to prioritize a combination of three indications without prior history of corrosion (i.e. moderate DCVG in conjunction with a severe DCI and a severe CIPS indication).
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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 | 0.029 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".