Acoustic treatments with innovative panels produced from olive oil wastes
Bibliographic record
Abstract
In the Mediterranean region, placing olive tree is a common practice. Historically, these trees were cultivated along the coasts of Magna Graecia, and today the European community is the largest global producer of olive oil. Spain accounts for 42% of production, followed by Italy with 17% and Greece with 11%. Olive oil has been traditionally extracted for food, cosmetics, medicine, and, in ancient times, also for lighting. One significant by-product of olive cultivation is the waste generated from cut tree trunks and foliage. This study aims to describe the possibility of utilizing these waste materials for sound absorption applications. The proposed system for the acoustic absorption involves triturating small trunks into particles ranging from 1.5 mm to 4 mm. The absorption coefficients were measured for samples with varying thicknesses, providing insights into the potential of these materials for sound absorption. This approach highlights a sustainable pathway for repurposing agricultural waste. Moving from the Delany-Bazley model, this study compares the impedance tube results with the theoretically predicted ones. Finally, using a least-square fit procedure based on the Nelder-Mead method, the coefficients that best predict both the acoustic impedance and the propagation constant laws are reported.
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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".