Physical and thermal characterizations of insulation composite made of wood fibers
Bibliographic record
Abstract
Currently, mineral fiber or cellular plastic insulation materials dominate the building construction market because of their relatively competitive price and low thermal conductivity properties; however, the environmental impacts of cellular plastic insulation materials during their whole life cycle are significantly higher. Additionally, the forestry sector in Canada generates approximately 5.38 × 106 oven-dried tons of wood product residues per year and there are opportunities to further process and use them as the raw materials for producing wood-based fiber insulation boards. The purpose of this study is to investigate the methods to utilize the woody residues and process them into wood fibers to form insulation boards. Additionally, an eco-friendly casein adhesive was chosen to bind the wood particles together. The boards were successfully formed and have thermal conductivity values ranging from 0.057 to 0.078 W/mK, based on different board densities and moisture contents, which satisfy the minimum requirement of the ASTM insulation standard. Thus, this study of forming wood-based fiber insulation boards shows opportunities of reducing the environmental impacts and reducing the building energy consumption due to heating or cooling at the same time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".