Bioenergy Conversion Potential of Decaying Hardwoods
Bibliographic record
Abstract
Unharvested hardwoods are abundant in eastern Canada, due to the low quality of their fiber and the absence of outlets in conventional wood transformation industries. The objective of this study was to assess the biochemical and thermochemical energy conversion potential of decaying hardwoods and compare their relationships with external and internal indicators of tree degradation. We characterized how wood-decay processes altered the physical and chemical properties of these woods and affected their digestibility yield and their performance according to indexes of stability and efficiency of combustion. DNA analysis on wood samples was also performed to determine the relative abundance of white-rot fungi compared to that of other saprotrophs. All properties stayed within the range of variations allowing the wood to remain suitable for conversion into bioenergy, even with increased decay. We found no significant differences in the physical and chemical properties that are crucial for energy production between wood from externally-assessed live and decayed trees. However, the proportion of wood area affected by rot was significantly associated with increased digestibility yield, and with decreased combustion reactivity. We could not detect any specific effect associated with increased relative abundance of white-rot fungi. These results suggest that the utilization of biomass from decayed hardwoods instead of live trees for bioenergy production should not alter the conversion efficiency and even potentially increase the performance of biochemical pathways, and hence, support their use as feedstock for bioenergy production.
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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".