Effect of extractives on conferred and natural durability of
Bibliographic record
Abstract
• Identification of extractives present in Cupressus lusitanica heartwood has been conducted using GC-MS analyses. The chromatogram of toluene/ethanol extracts indicated the presence of large amounts of benzaldehyde and numerous terpenic compounds such as cedrol, agathadiol, epimanool, bornyl acetate, α-cedrene and β-cedrene. • The effect of these extractives on the natural durability of cypress wood was investigated on heart wood blocks exposed to pure culture of Poria placenta before or after solvent extraction. Weight losses revealed severe fungal degradations on the extracted blocks compared to unextracted ones. • Efficiency of heartwood extractives as inhibitors of the growth of Poria placenta on malt/agar test confirms their contribution to cypress natural durability. • Cypress blocks were treated at 240 °C for different times to reach different levels of thermo-degradation to evaluate effect of heat treatment on fungal durability. Results indicate that evaporation of volatile extractives during the first few minutes of heat treatment contribute to decreased wood durability, while longer treatment times lead to the expected improvement of durability. • This study suggests that the content of extractives, which may be modified during wood drying or weathering processes, could be the origin of the conflicting data described in the literature concerning cypress natural durability.
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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) | 0.001 | 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".