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 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 | 0.000 |
| 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 | 0.000 |
| 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 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".