Guidelines for the Collection of Reliable and Practicable ECDA Indirect Inspection Data
Bibliographic record
Abstract
Abstract In recent years, pipeline leaks in the energy and infrastructure sectors have mandated asset owners to implement more regulated inspection regimes. NACE SP05021 – Pipeline External Corrosion Direct Assessment (ECDA) Methodology was created as a guide to apply the ECDA process and mitigate external corrosion on typical pipeline systems. The collection of reliable and practicable field data is crucial in the success of an ECDA process. Techniques and guidelines to obtain quality data in the field across the indirect inspection processes are presented. Some of the datasets discussed include Close Interval Potential Survey (CIPS) data, Direct Current Voltage Gradient (DCVG) data, soil resistivity data, AC induced corrosion data and pipeline interference data. Continued diligence of the surveying field personnel, as well as adherence to guidelines, will eventually save pipeline owners unnecessary excavation costs while also ensuring locations at the highest risk of external corrosion are rehabilitated and repaired as part of the direct examination.
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.
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 | 0.105 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.017 |
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".