Low-Constraint Toughness Testing of Two SE(T) Methods in a Single Specimen
Bibliographic record
Abstract
Single-edge notched tension (SE(T) or SENT) specimens has been increasingly proposed as a low-constraint toughness test to measure toughness of line pipe materials, as the crack tip constraint approximates a circumferential surface flaw in a pipe under loading. The clamped SE(T) single-specimen procedures recently developed by Shen and Tyson [1, 2] and Tang et al. [3] have in common the use of a clamped single-specimen of similar geometry and rely on unloading compliance technique for crack size estimation. In the former case, a single clip gauge is attached to the integral knife edge and the crack-tip opening displacement (CTOD) is estimated by means of a J-integral-to-CTOD conversion, similar to the procedure of ASTM E1820. The latter uses a pair of clip gauges mounted to an attachable raised set of knife edges to estimate CTOD at the original crack tip position by a triangulation rule. Consolidating these two sets of clip gauges in a specimen makes direct comparisons of two SE(T) methods on identical test conditions: material, specimen geometry, equipment, test temperature and operator [4]. In this study, SE(T) testing employing these two SE(T) methods on a single specimen was conducted on BxB shallow-cracked (a/W∼0.35) specimens of two x70 pipeline girth welds. This paper discusses the details of two SE(T) methods and techniques on the same specimen.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".