J-Resistance Results From Multi-Specimen and Single-Specimen Surface Notched SEN(T) Geometry
Bibliographic record
Abstract
In a collaborative program, J-R results were compared from multi-specimen and single-specimen procedures for toughness testing with single-edge notched tensile loaded SEN(T) specimens. Grade 483 MPa (X70) grade pipeline steel was used to prepare surface notched axially loaded specimens. Test procedures followed the multi-specimen method and a single-specimen recommended practice recently developed at the CANMET Materials Technology Laboratory (recently name-changed to CanmetMATERIALS) in a program jointly funded by PERD, PRCI, and DOT. The multi-specimen method adopted side grooves with the objective of comparison with results from the single–specimen method. The specimen geometry was B × B for both test procedures. The target ao/W was about 0.5. The multi-specimen testing was performed at BMT Fleet Technology. The single-specimen testing was performed at the CANMET laboratory, formerly located in Ottawa. The paper describes experimental and analysis details, and compares results from the two techniques, using J expressions developed at CANMET. The results showed similar J-resistance curves at small crack extensions for both techniques, and higher J-resistance values at larger crack extensions for the multi-specimen method. The results are discussed in terms of initial crack length and the analysis methods adopted for the two techniques. Validity criteria according to ASTM E1820 were applied and the results are presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".