Wood biomass supply model for bioenergy production in northwestern Ontario
Bibliographic record
Abstract
Wood biomass procurement for bioenergy production in an economic and sustainable way is a complex problem as it involves conflicting objectives of minimizing cost and distance of procurement, and maximizing quality of biomass, which is measured in terms of its moisture content. The multi-objective optimization problem is solved through pre-emptive goal programming approach using LINGO 11 software, where the cost of procurement is given the first priority, distance of procurement the second priority, and quality of biomass the third priority. The use of the model is demonstrated using a realistic example for bioenergy production for the recently established Abitibi-Bowater Inc. power plant at Fort Frances in northwestern Ontario, Canada, which has a weekly demand of 13,000 green tonnes for 50 Megawatt power production. The model selects quantity of biomass to be procured from each of the three zones ranging from 0–50 km, 50–70 km, and 70–100 km to meet the weekly demand.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".