Technological Characterization and Use of Babassu Residue (Orbygnia phalerata Mart.) in Particleboard
Bibliographic record
Abstract
The wood-based panel industry is growing all over the world, and in many countries the raw material is becoming increasingly inadequate and increasingly expensive. Therefore, new inputs are needed to reduce production costs. The residue of babassu palm fruit after nut extraction process is an alternative, due to its fibrous characteristics, the high availability in the large agroindustrial complexes in the North of Brazil and the low cost. Thus the research was: (i) to determine the mechanical characteristics (tensile strength and elasticity), physicochemical characteristics (density, pH, extractives content and chemical composition) and micro-structural of the babassu residue, comparing it with the particles of Pinus sp., used in commercial panels; (ii) to evaluate the quality of panels manufactured with babassu residue by physical conditions (swelling in thickness and water absorption) and mechanical characterization (elasticity and rupture) based on Brazilian Technical Standards (NBR 14810-2: 2013), American (ANSI A208.1: 2009) and European (EN 312: 2003). Statistical analysis of the data was performed with the aid of Infostat software through analysis of variance using Tukey test at the 5% probability level. The babassu residue has technological characteristics that allow its use in particleboard production. However, there are particularities that may compromise its use, such as high silica content and high density. Particleboard made from babassu residue presented mechanical performance above that required by current normative documents. However, for the physical properties, especially for the porosity of the particleboard of babassu, the obtained values were lower than the required minimum.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".