Modeling the woody biomass supply chain for energy production in northwestern Ontario
Bibliographic record
Abstract
Efficient procurement and optimal utilization of woody biomass for bioenergy production requires a good understanding of biomass supply-chain management. The general objective of this research is to develop decision support models for analyzing and aiding decision-making for optimal woody biomass supply chain management for energy production in northwestern Ontario (NWO). The specific objectives are: exploration of data sources and methods for assessing woody biomass availability, assessment of availability of woody biomass feedstock for energy production in the forest management units (FMUs) of NWO; development of a road network optimization model to optimize woody biomass feedstock transportation from forest cells (1 km x 1 km grid) to power plants; and development of optimization models for analyzing the optimal woody biomass supply from forest cells to one power plant with monthly production schedules (dynamic mathematical programming model) and to four competing power plants (modeling the woody biomass competition issues) in NWO. \nThe spatial assessment study found that in the 19,315 depletion cells (the forest areas where some level of timber harvest took place during 2002-2009) within the study area about 2.1 million green tonnes (gt) of forest harvest residue and 7.6 million gt of underutilized wood are technically available, which is enough to supply the annual biomass demand (2.21 million gt) of the four power plants were they to convert to using only renewable energy sources.
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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 | 0.001 | 0.001 |
| 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".