Determination of Critical Hydrogen Curves From Slow Bend Tests
Bibliographic record
Abstract
Recent trends in the pipeline industry are towards the use of high strength steels. As steel strength increases, the delayed hydrogen cracking propensity in the welds also increases. As welding is often completed during winter months, the cold temperatures must be considered in determining joining procedures that will avoid delayed hydrogen cracking. The Graville/BMT Fleet Technology Limited hydrogen diffusion and cracking models have been used successfully in the past to predict delayed cracking and to demonstrate how changes implemented in the welding procedure can minimize the risk of cracking. The two capabilities, hydrogen diffusion and cracking assessment, can be applied to the case of X100 pipe as well, provided the hydrogen diffusivity and the hydrogen cracking susceptibility curves are established for the materials of interest. These two parameters, the hydrogen diffusivity and the hydrogen cracking susceptibility curves are developed to examine the hydrogen cracking susceptibility of SMAW and GMAW welds in X100 pipe, and are the focus of the paper.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".