Technical Performance Overview of Bio-Based Insulation Materials Compared to Expanded Polystyrene
Bibliographic record
Abstract
The energy efficiency of buildings is well documented. However, to improve standards of energy efficiency, the embodied energy of materials included in the envelope is also increasing. Natural fibers like wood and hemp are used to make low environmental impact insulation products. Technical characterizations of five bio-based materials are described and compared to a common, traditional, synthetic-based insulation material, i.e., expanded polystyrene. The study tests the thermal conductivity and the vapor transmission performance, as well as the combustibility of the material. Achieving densities below 60 kg/m3, wood and hemp batt insulation products show thermal conductivity in the same range as expanded polystyrene (0.036 kW/mK). The vapor permeability depends on the geometry of the internal structure of the material. With long fibers are intertwined with interstices, vapor can diffuse and flow through the natural insulation up to three times more than with cellular synthetic (polymer) -based insulation. Having a short ignition times, natural insulation materials are highly combustible. On the other hand, they release a significantly lower amount of smoke and heat during combustion, making them safer than the expanded polystyrene. The behavior of a bio-based building envelopes needs to be assessed to understand the hygrothermal characteristics of these nontraditional materials which are currently being used in building systems.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".