Effects of environmental conditions on physical and mechanical properties of thermally modified wood
Bibliographic record
Abstract
The behaviour of industrially modified wood has not been systematically evaluated in controlled exposure conditions. The objective of this study was to assess the equilibrium moisture content (EMC), dimensions, and Brinell hardness of thermally modified wood in different conditions of temperature and relative humidity (RH). Tested materials consisted of European ash (Fraxinus excelsior L.), Norway spruce (Picea abies (L.) Karst.), and Scots pine (Pinus sylvestris L.) that were thermally modified according to ThermoWood industrial processes into the classes Thermo-S and Thermo-D. The properties were measured at the following conditions: 20 °C and 65% RH, 10 °C and 90% RH, and 30 °C and 30% RH. The results show that the reduction of EMC and the improvement in dimensional stability are dependent on the degree of thermal modification. Thermal modification was more resistant to moisture absorption at 20 °C and 65% RH than at 10 °C and 90% RH and 30 °C and 30% RH, and the more severe modification decreased the difference among different exposure conditions. The tangential–radial ratio of swelling and shrinkage was higher for thermally modified wood than for nonmodified wood. Brinell hardness of modified Scots pine and Norway spruce did not differ significantly from that of nonmodified wood in normal and dry conditions, but the more humid conditions increased the difference by 12%–17%.
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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 | 0.000 | 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) | 0.001 | 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".