Creep behaviour of HDPE/wood particle composites
Bibliographic record
Abstract
The effect of particle type, size, content and manufacturing process on the creep behaviour of wood particles/High Density Polyethylene (HDPE) composites has been investigated. Short-term creep tests at different temperatures were carried out and modelled using the Bürger's model and the Findley power law. The creep of the composites was found to increase with temperature due to the mobility of the amorphous bulk and tie HDPE molecules. Increased wood particle content generally decreased the creep level. Jack pine composites exhibited the highest creep reduction due to the chemical composition of the fibres surface and the efficiency of adhesion mechanism between fibres and the HDPE. Injection and compression processes led to better creep behaviour than the extrusion process due to differences in the composites microstructures. Particle size did not show important impacts on the creep properties. Findley power law led to better prediction of long time creep behaviour of the composites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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 teacher head, 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".