Multivariate modeling of MDF panel properties in relation to wood fiber characteristics
Bibliographic record
Abstract
Abstract Properties of medium density fiberboard (MDF) panels in relation to wood and fiber characteristics were investigated. Laboratory MDF panels were manufactured from raw fiber materials from black spruce [Picea mariana (Mill.) BSP.], three hybrid poplar clones (Populus spp.), two exotic larch (Larix gmelinii and Larix sibirica), and a mix of spruce, pine, and fir wood chips. The panels were evaluated for modulus of rupture (MOR), modulus of elasticity (MOE), internal bond (IB), linear expansion (LE), thickness swelling (TS), and water absorption (WA). These properties were analyzed as response variables. As predictor variables, various wood and fiber characteristics were measured, including wood density, pH, base buffering capacity and fiber coarseness. Multiple linear regression analysis was performed to develop functional relationships between panel properties (response variables) and wood fiber characteristics (predictor variables). Ten dummy variables were created and incorporated into the analysis to examine the effects of wood species or type on MDF panel properties. MOR was negatively related to arithmetic fine percentage. MOE was negatively affected by the percentage of small particles (>200 mesh) and wood pH. IB strength was negatively related to arithmetic fine percentage and fiber pH, but positively related to the percentage of small particles (>200 mesh). Wood density affected LE. TS was negatively affected by arithmetic mean fiber length. Arithmetic mean fiber width had a negative effect on panel WA. The presence of dummy variables in the models for MOE, IB and LE indicates that wood fiber characteristics other than those measured in this study significantly affected these panel properties. The study indicates that the refining process can play a significant role in manipulating MDF panel properties.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".