Experiments on Mechanical Properties of Bio-Composite Produced from the Shell of Argan Nuts
Bibliographic record
Abstract
The argan tree (Argania Spinosa) grows mainly in Morocco.This forest species, also called iron tree, covers about 830000 ha of Moroccan territory.Virtues of this argan oil are multiple: uses for cooking, medicines, or cosmetics.The objective of this work is the valorization of the argan tree, and especially in its shell, which remains poorly exploited and which is sold by the premises at low prices for use as a fuel in the Moorish baths and bakeries.Argan shell is the reinforcement of this bio-composite.For this, the shell is ground, sieved and separated into five different diameters d.Then, the preparations of the three ranges, each of which is composed of proportions (20%, 60%, 20%) of increasing diameter of particles.Each particle range is used as reinforcement in a matrix of urea formaldehyde and water as a non-polluting solvent.Using a traction machine, the Young module and the breaking stress of the three biomaterial ranges were determined.Our results show that the Young module of first range (R1) remains superior to that of other two ranges and also has a high breaking voltage.Then the bending measurements were made by the three-point machine MTS EM Flexion.Our results show that the R1 has a greater bending stress than the other two ranges.
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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) | 0.002 | 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".