Modeling Woody Biomass Procurement for Bioenergy Production at the Atikokan Generating Station in Northwestern Ontario, Canada
Bibliographic record
Abstract
Efficient procurement and utilization of woody biomass for bioenergy production requires a good understanding of biomass supply chains. In this paper, a dynamic optimization model has been developed and applied to estimate monthly supply and procurement costs of woody biomass required for the Atikokan Generating Station (AGS) in northwestern Ontario, based on its monthly electricity production schedule. The decision variables in the model are monthly harvest levels of two types of woody biomass, forest harvest residues and unutilized biomass, from 19,315 forest depletion cells (each 1 km2) for a one year planning horizon. Sixteen scenarios are tested to examine the sensitivity of the cost minimization model to changing economic and technological parameters. Reduction in moisture content and improvement of conversion efficiency showed relatively higher reductions in monthly and total costs of woody biomass feedstock for the AGS. The results of this study help in understanding and designing decision support systems for optimal biomass supply chains under dynamic operational frameworks.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".