Moisture sorption properties of hardwoods as affected by their extraneous substances, wood density, and interlocked grain
Bibliographic record
Abstract
Wood samples of nine tropical hardwoods from Peru and sugar maple from Quebec were selected for moisture sorption tests using a multiple step procedure at 25°C. Cold-water and hot-water extractives, sequential cyclohexane, acetone, and methanol extracts, ash content, wood density, and interlocked grain also were evaluated on matched samples. Wood extractives, interlocked grain, equilibrium moisture content (EMC), and hygroscopic stability were highly variable within and among wood species. Sequential extraction with organic solvents was the most suitable method for evaluating the effect of extractives on sorption behavior in tropical hardwoods. Cyclohexane extractives had a little influence on EMC. The acetone fraction was the most significant variable affecting EMC and hygroscopic stability of tropical hardwoods, while the methanol fraction had a negligible effect on the sorption behavior of these species. In general, EMC decreased and hygroscopic stability increased as the concentration of acetone extractives increased. The influence of these acetone-soluble compounds on EMC decreased as relative humidity increased. However, the acetone fraction of copaiba ( Copaifera sp.), and probably caoba ( Swietenia sp.), appeared to play a hydrophilic role in controlling the EMC of wood. Finally, EMC also decreased as wood density and interlocked grain increased.
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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 | 0.001 | 0.001 |
| 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".