Waste corn husk fibers for sound absorption and thermal insulation applications: A step towards sustainable buildings
Bibliographic record
Abstract
In the last decade, noise pollution and global warming and their effects on human health and the environment have received much attention. Building sectors are one of the most important areas for potential improvements. To this end, sound-absorbing and thermal insulation construction materials are being used effectively. Recently, a great deal of interest has arisen in using various natural fibers as sound-absorbing and/or thermal insulation materials . In line with these studies, this work investigates the acoustic absorption and thermal insulation characteristics of corn husk fiber (CHF). The results showed that the samples enjoy excellent noise reduction coefficients of 0.36–60 and effective thermal conductivities of 0.038–0.042 W/mK. It was found that the thermal insulation properties of CHFs are not significantly influenced by the moisture content . The Dunn and Davern (DD) model and a modified model of DD based on the Nelder-Mead simplex algorithm were also used to predict the acoustic behavior of the samples. It was found that the proposed model provides very excellent prediction accuracy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".