Impact of log position in the tree on mechanical and physical properties of black spruce medium-density fibreboard panels
Bibliographic record
Abstract
Mechanical and physical properties of medium-density fibreboard (MDF) panels made from black spruce ( Picea mariana (Mill.) BSP) top, middle, and butt logs were studied. The analysis of variance and analysis of covariance were both performed to examine the impact of log position in the tree on panel modulus of rupture (MOR), modulus of elasticity (MOE), internal bond (IB), linear expansion (LE), thickness swell (TS), and water absorption. Results indicate that MOE and IB strength of MDF panels made from top and middle logs were significantly superior to those of panels made from butt logs; however, there was no significant difference in MOE and IB between panels made from top and middle logs. Water absorptions of top and middle log panels were significantly lower than that of panels made from butt logs, and the difference in water absorption between panels made from top and middle logs was not significant. TS of top log panels was the smallest among the panels from the three log positions in the tree and was significantly different from those of middle and butt log panels. TS of butt log panels was the highest, which was significantly different from that of top and middle log panels. The differences in LE among the panels made from top, middle, and butt logs were not significant. The comparison of MOR of top, middle, and butt log panels was dependent on panel density because of the interactions among the three groups. Top and middle log panels showed superior properties, because the thinner cell walls of fibres from top and middle logs resulted in an increased compaction ratio compared with the butt log panels. Panel density affected both panel MOR and MOE considerably; however, its impact on IB, LE, TS, and water absorption was not significant in this study. The equations describing the linear relationships between MOR, MOE, and panel density were developed.
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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 | 0.001 |
| 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 | 0.001 | 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".