Analysis of tensile test results for poly(acrylonitrile‐butadiene‐styrene) based on Weibull distribution
Bibliographic record
Abstract
Abstract Statistical analysis based on two‐parameter Weibull distribution was applied to poly(acrylonitrile‐butadiene‐styrene) to characterize involvement of two damage types in tensile test specimens, which appear as tiny strips and uniform whitening, respectively. Analysis using Weibull distribution suggests that the probability density functions (PDF) for extension at break and total energy consumption (named toughness) give distinctively different characteristics between the two damage types. PDF curve for the tiny strips consists of a sharp peak, whereas that for the uniform whitening a broad hump. Using two‐group mixed Weibull distribution, effect of pre‐existing damage on the fracture process was examined. The PDF curves show that presence of one damage type (e.g., uniform whitening) tends to suppress generation of the other damage type (e.g., tiny strips) even in a test condition that would have favored the latter in virgin specimens. Involvement of the two damage types was quantified using portion for each subpopulation from the Weibull distribution analysis. POLYM. ENG. SCI., 2011. © 2011 Society of Plastics Engineers
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".