Using Operational Research for Supply Chain Planning in the Forest Products Industry
Bibliographic record
Abstract
Over the years, Operational Research (OR) has been used extensively to support the forest products industry and public forestry organizations (e.g., governments, environmental protection groups) in their respective planning activities concerning the flow of wood fiber from the forest to the customer. The applications deal with a wide range of problems, ranging from long-term strategic problems related to forest management or company development to very short-term operational problems, such as planning for real-time log/chip transportation or cutting. This paper presents an overview of the different planning problems and reviews the past contributions in the field of forestry, with a focus on applications and problem descriptions. In the context of the 50th anniversary of the Canadian Operational Research Society, this paper also recognizes the contributions of many Canadian researchers to the field of forestry management.
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".