Validation of the SDAR Algorithm and ASTM Standard Practice E3076
Bibliographic record
Abstract
Many ASTM standards (for example, ASTM E8, Standard Test Methods for Tension Testing of Metallic Materials; ASTM E9, Standard Test Methods of Compression Testing of Metallic Materials at Room Temperature; and ASTM E111, Standard Test Method for Young's Modulus, Tangent Modulus, and Chord Modulus) require slope determination as part of data analysis but do not specify an objective method for making this determination. This lack of method leads to increased variability, which impacts precision and bias. In 2007, work began to develop an algorithm that would perform an objective determination of slope. That algorithm became known as SDAR (Slope Determination by Analysis of Residuals). In 2011, ASTM Committee E08 on Fatigue and Fracture began developing a draft standard practice based on the SDAR algorithm. Because implementation of the algorithm is computationally intensive, there were concerns about its complexities. The ability of users to code the algorithm in the language or application of their choice was evaluated by way of two analytical round robins. The results of those two round robins are here presented and the conclusions derived from them are discussed.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.028 |
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".