Fuel Properties of Sugar Maple and Yellow Birch Wood in Relation with Tree Vigor
Bibliographic record
Abstract
The fuel properties of wood obtained from sugar maple (SM) and yellow birch (YB) of temperate hardwood stands located in the Province of Qubec, Canada were studied to see how tree vigor affects the chemical composition and calorific value of the wood. This study focused on the physical and chemical properties of wood with the aim of using the material for the production of solid biofuels. Specific items measured included the wood's calorific values, and the levels of extractives, ash, and lignin. Changes in chemical composition were found among tree vigor classes. The low vigor trees had higher extractives, ash, and lignin contents than the vigorous trees. Total extractives ranged between 4.88 and 7.32% in SM, and between 3.35 and 5.12% in YB. Klason lignin ranged between 21.46 and 23.53% in SM, and between 18.60 and 21.51% in YB. Ash content ranged between 0.38 and 0.97% in SM, and between 0.26 and 0.47% in YB. The combined effects of higher lignin content that could contribute to a better self-bonding of particles and of higher extractives content that could facilitate the pelletization process makes the low vigor trees more suitable for conversion into solid biofuels. The higher amounts of extractives and lignin present in the low vigor sugar maple and yellow birch trees could also have a positive role in maintaining the high calorific values of this wood despite higher ash content.
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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 | 0.001 | 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".