PR261-193604-R01 Optimizing Stress Corrosion Cracking Management - Field and Economic Study
Bibliographic record
Abstract
This work aims to improve pipeline segment prioritization for stress corrosion cracking (SCC) excavations. Specifically, it is aimed at optimizing the technical accuracy and the cost of the Association for Materials Protection and Performance (AMPP, formerly NACE) Stress Corrosion Cracking Direct Assessment (SP0204-2015) process by: - evaluating the SCC susceptibility criteria of soil property parameters that were proposed in the first phase of the project (pH, resistivity, sulfide concentration, soil carbon dioxide (CO2) concentration, carbonate concentration, soil oxygen (O2) concentration, sulphate reducing bacteria (SRB) concentration, oxygen reduction potential (ORP), soil moisture content, soil effect on steel hydrogen permeation and electrochemical properties) - investigating the technical and economic feasibility of using commercially available field instruments for the measurement of these soil parameters to overcome limitations of laboratory testing (e.g., sample preservation and external costs) Soil sampling and testing was conducted at twenty-two dig sites in three geographic regions in the USA and Canada. On-site soil sampling and testing activities were conducted by field service providers using commercially available portable instruments. Soil samples were sent to laboratories for chemical analysis and for electrochemical characterization. The data analysis consisted of: (i) comparison of soil properties obtained at sites with and without SCC against the proposed SCC susceptibility criteria (ii) comparison of soil property data obtained in the field to those obtained through laboratory analysis (iii) comparison of soil property data obtained using different field and lab measurement techniques (iv) comparison of costs associated with performing in-field measurements to those of laboratory analysis.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".