Heterogeneity of Forest Harvest Residue from Eastern Ontario Biomass Harvests
Bibliographic record
Abstract
Abstract The environmental and economic problems associated with the use of fossil-based fuels have encouraged a shift to abundant renewable resources, such as forest residues. However, before forest residues can be used as an industrial resource, their properties must be known. This study determined the physical (moisture content, bulk density, and wood/bark ratio) and thermal (elemental composition, thermal reactivity, and energy value) properties of heterogeneous residues generated during commercial harvesting on two forest sites within the Great Lakes St. Lawrence forest of southeastern Ontario. Other factors that can affect these properties, such as duration of storage and location in a storgae pile, were also evaluated. A physical fractionation treatment was also investigated as a means of value addition to forest residues. Long-term storage in an uncovered pile resulted in the forest biomass on the surface losing moisture (19.3%) and the biomass on the inside gaining moisture (73.1%). In addition, storage caused an increase in bulk density and a reduction in chloride content. The higher heating value of the forest harvest residues averaged 19.0 MJ/kg (standard deviation [SD] = 0.3 MJ/kg), with an average energy density of 1,991 MJ/m 3 (SD = 628 MJ/m 3 ). This study also found that size fractionation resulted in fractions with more uniform properties.
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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 | 0.001 | 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".