Effect of the Varying Percentage Diss Fiber on Mechanical Behaviour of the Based Polyester Bio-Composite
Bibliographic record
Abstract
Improving the mechanical and physical properties of bio-composite materials involves the incorporation of plant fibers such as Jute, Hemp, Kenaf, Ramie, Sisal, Linen, etc. The existence of Diss grass (Ampelodesmos mauritanicus) in abundance in the east of Algeria especially in Khenchela region and taking into account their mechanical resistance and their low density, which justifies their choice of use in composite materials. Tensile and hardness tests for different volume fractions (from 05% to 20%) of short fibers of Diss are performed. The increase in fiber content and their treatment improves the mechanical characteristics of the composite materials. These concentration levels are added to a Polyester resin matrix. Our work relates to the study of a composite material reinforced by a vegetable fiber of which different volume ratio of short Diss fiber are considered. The results collected are purely experimental.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".