UTILIZAÇÃO DE MADEIRAS DE Eucalyptus grandis E Eucalyptus dunnii PARA PRODUÇÃO DE PAINÉIS DE PARTÍCULAS ORIENTADAS - OSB UTILIZATION OF WOOD OF Eucalyptus grandis AND Eucalyptus dunnii FOR ORIENTED STRAND BOARD - OSB MANUFACTURING
Bibliographic record
Abstract
This study was developed to evaluate the feasibility of OSB manufacturing using woods of Eucalyptus grandis and Eucalyptus dunnii. Boards with nominal density of 0,70 g/cm³ and 1,0 g/cm³ were manufactured in laboratory, using 100% of wood particles from Pinus taeda, Eucalyptus grandis and Eucalyptus dunnii, and mixtures of 50% of Pinus taeda in the internal layer of the board, with 50% of Eucalyptus grandis and 50% of Eucalyptus dunnii. The boards of Eucalyptus grandis with density of 0,70 g/cm³, as standard board density, showed the values of properties compatible with the requirements of the Canadian and European Standards and also in relation of boards manufactured from Pinus taeda. The results of the mechanical properties showed an increase in the MOE and MOR in static bending with the increase in the board density, opening the possibility to use the high density OSB for applications requiring higher strength. The results of this research indicate that wood of Eucalyptus grandis can be used as alternative specie to OSB manufacturing in the Brazil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".