Bibliographic record
Abstract
This paper presents results from an experimental investigation about the influence of loading history on the elastic modulus of high-density polyethylene (HDPE). The study uses a new approach, named two-test method, to introduce the loading history using the first test and at least one month later, to measure the elastic modulus using the second test. The period of minimum one month between the two tests is to ensure that recovery of the viscous deformation from the first tests does not affect the measured elastic modulus from the second tests. Parameters considered to vary the loading history include maximum deformation (with the area strain up to 0.5 in most cases), loading mode (monotonic, cyclic and creep tension), test speed (from 0.005 to 30 mm/min), and stress triaxiality (through the change in the specimen gauge length), in which the area strain is defined as the logarithmic ratio of the original cross sectional area (i.e., the cross sectional area of virgin specimen) to the deformed cross sectional area. All of the second tests were conducted under monotonic tension at the crosshead speed of 0.001 mm/min. The results show the general trend of decrease of the elastic modulus, measured from the second tests, with the increase of the strain applied in the first tests. The results further indicate that test speed for deformation introduced in the first tests may have a significant influence on the elastic modulus measured from the second tests. The elastic modulus is used to determine damage parameter D based on the damage concept, based on influence of the loading history on the mechanical properties of HDPE is characterized.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".