Modeling the importance of biomass qualities in biomass supply chains for bioenergy production
Bibliographic record
Abstract
A tactical-operational level quantitative model can be an important decision support tool for bioenergy producers. Goal programming approach can help analyze the costs and volume implications of various competing goals in terms of biomass characteristics on part of the bioenergy producers. One cost and six quality characteristics goals, namely moisture and ash contents, and thermal values of two types of biomass (forest harvest residue and un/under-utilized species) are selected for the four bioenergy producers in northwestern, Ontario, Canada. We run four models cenarios: i) benchmark total cost and ceilings of mean values of six biomass qualities (Initial Goals), ii)relaxing the quality goals by 10% from the Initial Goals scenario, iii) increasing the conversion efficiency by 10%, and iv) all goals as in Initial Goals except the Atikokan Generating Station (AGS)being supplied with only un/under-utilized biomass. The smaller power plants have relatively less per unit biomass procurement cost. While per unit procurement costs increased, the total costs and biomass volume required to produce the same amount of bioenergy for each power plant decreased in all scenarios compared to the benchmark costs. The goal programming approach, and the results thereof are found to be useful in making effective decisions in the biomass supply chains for bioenergy production.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".