Physical and Mechanical Assessments Of Fire Retardant-Treated Shorea macrophylla And Acacia mangium Particleboards
Bibliographic record
Abstract
Physical and mechanical properties of fire retardant-treated engkabang (Shorea macrophylla) and Acacia mangium particleboards were assessed. Tests selected for the assessments were water absorption, thickness swelling, modulus of rupture (MOR), modulus of elasticity (MOE) and internal bond (IB). The engkabang and Acacia mangium particles were mixed with 10% w/w of two different fire retardants namely zinc borate (4Zn0.6B2O3.7H2O)-simplified as ZBr and monoammonium phosphate (NH4)H2PO4) during the mixing stage of the particleboard manufacturing processes. The properties of the particleboards were tested using British European 1993 standard. An analysis of variance was carried out to study the effects of fire retardant types on the boards of both species. The study showed that MAP-treated particleboards were superior to ZBr-treated particleboards for both mechanical and physical performances except for modulus of elasticity (MOE). Overall, the fire retardants reduced the physical and mechanical properties of the engkabang and Acacia mangium particleboards. However, all treated and untreated particleboards complied with the standard requirement values and these findings are expected to increase the promotion of both species to be used in producing fire-retardant particleboards.
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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 | 0.000 | 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".