Effects of two-year weather exposure on thermally modified <i>Picea abies</i>, <i>Pinus sylvestris</i>, and <i>Fraxinus excelsior</i> wood
Bibliographic record
Abstract
The use of thermally modified timber (TMT) in outdoor applications is well established, but its performance against the stresses of outdoor conditions is not comprehensively understood. This study investigates the changes in density, surface chemical composition, color, equilibrium moisture content (EMC), checking, hardness, and cupping of thermally modified boards of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst.), and European ash (Fraxinus excelsior L.) during a two-year weather exposure test in Eastern Finland. Unmodified pine, spruce, and ash and copper salt impregnated pine boards were used as controls. The results show that a two-year weather exposure period causes degradation of hydrophobic lignin, and the leaching of the degraded compounds changed the color, increased the EMC, and decreased the hardness of specimens. Although the EMC of TMT specimens was lower compared with unmodified and impregnated ones during weathering, the changes were more obvious in TMT than in the controls. More checks occurred in the TMT specimens than in unmodified ones after the exposure. On the other hand, the TMT specimens had a lower degree of cupping. These findings also indicate that an increase in modification temperature induces more checks but improves the durability of all studied species by reducing the chemical degradation, color change, cupping, and hygroscopicity during weather exposure.
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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".