Metal Oxide Sol-Gels (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mtext>ZrO</mml:mtext></mml:mrow><mml:mn mathvariant="bold">2</mml:mn></mml:msub></mml:mrow></mml:math>, AlO(OH), and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msub><mml:mrow><mml:mtext>SiO</mml:mtext></mml:mrow><mml:mn mathvariant="bold">2</mml:mn></mml:msub></mml:mrow></mml:math>) to Improve the Mechanical Performance of Wood Substrates
Bibliographic record
Abstract
Wood is a renewable material widely used in many applications due to its unique properties and distinctive look. However, as wood is organically constituted, it is slowly destroyed by the long-term impact of oxygen, UV radiations, water, and biological attacks (Mahltig et al., 2008). Therefore, protective treatments are necessary to improve the mechanical, thermal, and chemical properties of wood. In order to improve the mechanical properties of sugar maple (Acer saccharum Marsh.), as this species is widely used in the wood products industry, samples of sugar maple were impregnated with sols of metal oxides (AlO(OH), SiO2, and ZrO2). The weight gain and two different techniques of microscopy were used to evaluate the efficiency of the impregnation in the wood samples. The mechanical properties were evaluated using hardness test, scratch test, and impact test. It was shown that the maple samples impregnated with ZrO2 led to the greatest improvement of the mechanical properties.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".