Effect of juvenile wood on strength properties and dimensional stability of black spruce medium-density fiberboard panels
Bibliographic record
Abstract
Abstract Strength properties and dimensional stability of medium-density fiberboard (MDF) panels made from black spruce (Picea mariana [Mill.] BSP.) 0–20, 21–40, and over 40 year old fiber were studied. An analysis of covariance (ANCOVA) was performed to examine the differences in modulus of rupture (MOR), modulus of elasticity (MOE), and thickness swell (TS) of the three types of panels, while panel density was treated as a covariate in order to adjust the mean values that were partly attributed to panel density. The results indicate that MOR, internal bond (IB), and water absorption of MDF panels made from 0–20 year old fiber, which contained 100% juvenile wood, were significantly superior to those of panels made from 21–40 and over 40 year old fiber; but linear expansion (LE) of MDF panels made from 0–20 year old fiber was significantly larger than that of panels from the other two age classes. The differences in MOR, IB, water absorption, and LE between panels made from 21–40 and over 40 year old fiber were not significant. The comparisons of panel MOE and TS were relatively dependent on panel density due to existence of interactions among the three age groups.
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 | 0.001 |
| 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 | 0.000 | 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".