Characterization of sugar maple wood-polymer composites: Monomer retention and polymer retention
Bibliographic record
Abstract
Abstract In this study, sugar maple samples were impregnated with three impregnants by a vacuum and pressure process and in situ polymerized by a catalyst-thermal procedure. The effects of polymeric monomers and their combinations on monomer retention by volume (MRV) and weight (MRW) and polymer retention (PR) were analyzed and the wood was examined by scanning electronic microscopy (SEM). The three methacrylates chosen as impregnants were methyl methacrylate (MMA), hydroxyethylene methacrylate (HEMA) and ethylene glycol dimethacrylate (EGDMA). The formulation combinations were determined by a mixture design. MRV was similar, regardless of formulation combinations, which indicated that these methacrylates show similar permeability into sugar maple. However, MRW was different between formulation combinations due to differences in monomer density. MRV was inversely related to wood density. PR was related to the combination of impregnants and inversely related to wood density. The impregnants resided in the vessel and the lumen. The morphology of polymers in the wood was different for different treatments due to the distinct molecular structures formed from each polymer.
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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".