Woody biomass availability for bioenergy production using forest depletion spatial data in northwestern Ontario
Bibliographic record
Abstract
Woody biomass contributes about 6% of total energy production in Canada. One obstacle to the adoption of woody biomass for energy production is accurate data on sustainable supply. The purpose of this study is to demonstrate the assessment of woody biomass annually available for bioenergy production. The study area, located in northwestern Ontario, includes 18 forest management units (167 184 km2) and three existing and one proposed biomass-based power generating stations, with a potential annual demand of 2.2 million green tonnes (gt). First, pre- and post-harvest inventories were carried out to assess the availability of harvest residues. Second, two spatial database layers (land-use class and forest depletion) were developed. The pre- and post-harvest inventory data were combined with spatial data analysis to estimate woody biomass in each square kilometre of the study area. It was estimated that annually there was more than 2.1 million gt of forest harvest residue and 7.6 million gt of underutilized woody biomass technically available between 2002 and 2009 for bioenergy production, with an average annual forest depletion rate of 60 867 ha, 0.6% of the total productive forest area. The study provides a tool for assessing the sustainable availability of woody biomass feedstock for power generation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".