Integrated planning for a multiproduct, multisite reforestation value chain: a Canadian case study
Bibliographic record
Abstract
This paper addresses integrated supply and distribution planning for a multiproduct, multisite reforestation value chain (RVC) in a make-to-order context. The aim is to develop a tactical planning tool to determine the quantities of seedling species and sizes to order from nurseries to satisfy reforestation site needs while minimizing purchase and distribution costs. A mixed-integer linear programming model was developed and applied to the publicly managed RVC in the province of Quebec, Canada. This tool was also used to analyze scenarios considering the three pillars of sustainability: environmental (climate risk management to bolster resilience), social (fair production allocation), and economic (supply and distribution costs) aspects. This strategic analysis highlights the trade-offs between purely economic and more sustainable scenarios and discusses strategic deployment opportunities to improve the RVC. Ultimately, it serves as a powerful tool for efficiently allocating yearly seedling supply, minimizing costs, and promoting sustainable practices in reforestation planning which provides valuable insights for RVC managers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".