Recipe adaptation and new recipe development for high temperature heat treatment of North American wood species
Bibliographic record
Abstract
The thermal treatment of wood at high temperatures is an environment friendly and commercially viable alternative wood modification technology. In this process, wood is heated to temperatures above 200ºC. This modifies the structure of wood and improves its hardness, dimensional stability, and resistance to biological attacks compared to those of the untreated wood. Its color also becomes darker and more attractive. However, this treatment may cause a decrease in wood elasticity. Therefore, optimization of the treatment parameters is necessary for a quality product. In addition, the high temperature heat-treatment processes for wood were first developed in Europe, and the recipes used for the European species were not necessarily applicable to the North American species. Thus, adaptation of the technologies to the latter species was necessary. The industrialists in the Saguenay-Lac-St-Jean region of Quebec brought two heat treatment technologies (Bois Perdure from France and Thermowood from Finland) to Canada. The adaptation of the technology is a very costly procedure at industrial scale. The Research Group on the Thermotransformation of Wood (GRTB – Groupe de recherche sur la thermotransformation du bois) at the University of Quebec at Chicoutimi (UQAC) which works closely with these industries developed a method for adapting the existing recipes to the North American species as well as for developing new ones for other species. UQAC is the first North American university which has such a unique research structure to carry out this type of research. The recipe development starts in a laboratory scale furnace. The high temperature heat-treatment experiments are carried out in a thermogravimetric system under different conditions until a promising set of conditions is identified for the properties sought by the industry. Consequently, the trends are identified for a given species. To determine the properties, various characterization tests (bending, dimensional stability, screw withdrawal, etc.) are done. Then, the heat treatment trials in a prototype furnace are carried out to finalize the recipe. This is followed by trials in an industrial-scale furnace for validation of the results.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.013 |
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".