An alternative approach to estimating parameters in creep models of high‐density polyethylene
Bibliographic record
Abstract
Abstract Polyethylene (PE) is increasingly used in structural applications due to its light weight and rust‐resistant nature. With growing demand for the use of PE as a structural material, there is a need for mathematical models that describe the mechanical behavior of this material. Curve fitting using a linear time‐dependent model is a common approach for modeling creep of PE at the macrostructural level. However, besides the point estimates of the model parameters and the (visual) fit of the model to experimental data, little else is learnt from the curve‐fitting approach. This work presents a rigorous statistical approach for modeling creep compliance of PE. Four high‐density PE resins used over a wide range of applications are studied. Linear viscoelastic modeling using the multi‐Kelvin element theory is examined in two forms: model linear in parameters and model nonlinear in parameters. With the application of valid statistical techniques, complex relationships between model parameters, largely unstudied before, are observed, such as evidence of a high degree of correlation among material parameters of the creep model. POLYM. ENG. SCI., 2011. © 2010 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".