Mechanical relaxations in heat‐aged polycarbonate. Part II: Statistical analysis of low‐molecular weight data
Bibliographic record
Abstract
Abstract The significance of heat‐aging effects on low‐molecular‐weight polycarbonate has been studied by performing a two‐factor Analysis of Variance (ANOVA). Although this work was primarily motivated by the large experimental scatter observed in stress relaxation results for LMW 2608 (part I), the effect of heat‐aging on the characteristics of secondary transitions (γ and β 1 ) generated by dynamic testing was also investigated. Both types of tests were performed using a dynamic mechanical analyzer. The statistical analysis verified an earlier suggestion that both the secondary transitions were insensitive to heat‐aging. In the quasi‐static stress relaxation tests, the curve‐fitted KWW parameters (τ, E o ′ β′) were evaluated using ANOVA for increasing heat‐aging time and test temperature. Two other statistical techniques were also applied to test repeatability—the power of each aging time/test temperature combination and the number of observations needed to achieve 90% repeatability. In conclusion, both τ and β′ could describe the self‐retarding nature of volume recovery although the repeatability of β′ was substantially higher. However, the unrelaxed modulus, E o , was found to be an unreliable indicator of whether heat‐treatment had caused changes in the intrinsic structure. Overall, the study showed that the repeatability of the stress relaxation test results is generally very poor for the confidence levels tested.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".