Evaluation of the Effect of Camu Camu Peel on the Physical Properties of Asphalt Cement
Bibliographic record
Abstract
Oxidation in flexible pavements is a recurring issue in road engineering, primarily caused by moisture, temperature, and climatic conditions.This damage is observed globally, significantly affecting Europe with its low temperatures and the Americas with high temperatures, where some countries report up to 50% of their pavement deteriorated due to oxidation.A solution to this problem is the use of natural modifiers, which enhance the properties of asphalt cement, such as rice husk and blueberry fiber.This study evaluates the effect of Camu Camu peel (FCC), scientifically known as Myrciaria dubia, as an antioxidant additive on the physical properties of 60/70 asphalt cement.As one of Peru's richest fruits in antioxidants, thanks to its phenolic compounds, Camu Camu helps protect pavements exposed to various temperature conditions against aging.FCC was processed into a fine powder and incorporated in proportions of 2%, 5%, and 10% into asphalt mixtures, which were then subjected to penetration, ductility, and softening point tests before and after accelerated aging.The results indicate significant improvements in the physical properties and oxidation resistance of asphalt cement.The addition of 10% FCC increases ductility by 25%, improves penetration resistance by 28%, and raises the softening point by 26% after aging.Furthermore, the susceptibility to oxidation progressively decreases, highlighting an increase in the material's durability and functionality.
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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".