PHYSICAL AND MECHANICAL PROPERTIES OF PANEL BASED ON OUTER BARK PARTICLES OF WHITE BIRCH: MIXED PANELS WITH WOOD PARTICLES VERSUS WOOD FIBRES PROPIEDADES FÍSICAS Y MECÁNICAS DE PANELES A BASE DE PARTÍCULAS DE CORTEZA EXTERNA DE ABETO BLANCO: MEZCLA DE PANELES CON PARTÍCULAS DE MADERA VERSUS
Bibliographic record
Abstract
The use of outer white birch bark in canoes is an example of its oldest use by the fi rst nations in Canada. This use confi rms the hydrophobic characteristics of this bark, which can be capitalized on by using it in the outer layers of three-layer mixed composite panels in order to protect them from water infi ltration from their surface. These panels were made up of outer white birch bark particles in the surface layers with coarse wood particles or wood fi bres in the core layer. A factorial experiment used in a complete block design permitted to carry a suitable statistical analysis of measured properties. The two main considered factors were respectively the bark percentages in the surface layers with three levels and the type of material used in the core with two levels. Four replicates were done for each panel. The panels with wood particles in the core layer gave physical and mechanical properties satisfying the indoor requirements for particleboards and those with wood fi bres in the core layer passed the requirement of medium fi bres density board. Panel with 45% bark particles in the surface and 55% wood particles in the core was selected as the best because of its good dimensional stability.
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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 | 0.001 | 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) | 0.002 | 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".