Moisture and energy content of fire-burnt trees for bioenergy production: A case study of four tree species from northwestern Ontario
Bibliographic record
Abstract
With the current energy reform, the Ontario government has taken an initiative to phase out coal-fired generatingstations by 2014, and replace coal with biomass as feedstock at the Atikokan Generating Station. This switch to greenenergy production has opened a new avenue of income for mills and biomass-harvesting companies. However, as theneed for biomass increases, harvesting residues may no longer satisfy the needs of cogeneration facilities and new sourcesmay be sought. A potential source of woody biomass in Ontario is from forest fires. On average, an area of 35 460 ha or3 868 034 m 3 of wood is devastated by wildfire every year in the Area of the Undertaking in Ontario and has the potentialto be salvaged. However, the fuel quality and feasibility of salvaging wildfire-burnt areas for bioenergy production innorthern Ontario has not been investigated so far. In this study, five different-aged fires in the MNR Thunder Bay District—12, 18, 24, 37, and 52 months old—were sampled for moisture content and calorific value of the wood. This samplingwas done across four of the most prolific tree species grown in northwestern Ontario—white birch, trembling aspen,balsam fir, and black spruce. The average moisture content (dry weight basis) of the five fires ranged from 27.1% to 34.9%and the average calorific value from 19.0 MJ/kg to 21.1 MJ/kg. Significant differences in moisture content were foundbetween the species and the ages of fire. Hardwood species had significantly higher moisture content compared to softwoods.The results display that wildfire-burnt areas have the potential to supply good-quality fuel for bioenergy productionin northwestern Ontario. Key words: biomass, wildfire-burnt areas, bioenergy production, moisture content, calorific value, northern Ontario
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".