Insulation Properties of Rice-Based Materials in Hot and Moderate Climates
Bibliographic record
Abstract
Although many advanced insulation materials have been recently developed, very few are eco-friendly and their production requires a substantial amount of energy and complex manufacturing processes.To address this issue, a bio-based thermal insulation material was developed using short-and long-grained puffed rice.A set of experiments was subsequently carried out to identify the best rice type and the optimal range for the most influential parameters (sample amount, temperature, and moisture level).Our findings revealed that short-grained rice exhibited greater puffing ability and was thus adopted in further material optimization experiments.These assessments indicated that the most optimal thermal conductivity of the insulation material and the highest puffing ratio was attained at 12-15% moisture, 260-270C temperature, and 15-18 g sample weight.The thermal properties, including thermal conductivity and fire reaction, and thermal performance of samples obtained using these parameters were similar to those of common insulation materials.
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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 | 0.000 | 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) | 0.000 | 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".