Comparative study on the durability of heat-treated White Birch (<i>Betula papyrifera</i>) subjected to the attack of brown and white rot fungi
Bibliographic record
Abstract
The effect of heat treatment on decay resistance of white birch was evaluated for different incubation periods ranging from 2 to 12 weeks using three species of brown rot and one species of white rot fungus. The results of weight loss tests showed that the white rot fungus, Trametes versicolor, effectively degraded the untreated wood (73.5%). While the degradation of untreated wood by brown rot fungi species, Gloephyllum trabeum (11.6%) and Conifora puteana (6.2%), was considerably less compared to T. versicolor, the third brown rot fungi studied, Poria placenta, caused an appreciable degradation of the same species (52.4%). The results clearly showed that the heat treatment reduced the effect of fungi attack on white birch. Increasing the heat treatment temperature from 195 to 215°C resulted in reduction of weight loss, consequently, reduction in fungal attack. As an example, the weight loss reductions due to T. versicolor, P. placenta, G. trabeum and C. puteana attack was 62.2%, 71.3%, 89.6% and 100%, respectively, compared to the weight loss of untreated wood when it is heat treated at 215°C. Thus, these results confirmed that the heat treatment increased the biological resistance of white birch.
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 | 0.000 | 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) | 0.002 | 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".