Performances of white pine and white spruce treated with organic fungicides using an aqueous buffered amine oxide preservation system
Bibliographic record
Abstract
Wood is an environmentally friendly material for the construction of buildings, and it possesses great physical and mechanical properties. However, under certain circumstances, it needs to be protected from degradation. This can be achieved either by proper design or treatment. In this study, eastern white pine (Pinus strobus L.) and white spruce (Picea glauca (Moench) Voss) were impregnated with propiconazole and 3-iodo-2-propynyl butylcarbamate, which are two organic fungicides. Unlike most impregnation techniques, no pressure treatment was needed. Instead, an aqueous buffered amine oxide system was used to allow the fungicides to diffuse rapidly into the wood. Many combinations of fungicides and amine oxides, as well as different diffusion times were tested to study the effect of the treatment on the dimensional stability and resistance to decay fungi. It was found that only the amine oxide affected the dimensional stability of the treated wood, with anti-swelling and anti-shrinking efficiencies values up to 30%. Amine oxides and fungicides both had an impact on the weight loss caused by the brown rot fungi. The weight loss after 10 weeks of exposure to Rhodonia placenta was reduced by half when using amine oxides or fungicides, and it was completely inhibited when they were combined.
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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) | 0.001 | 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".