Fifty years of operational research in forestry
Bibliographic record
Abstract
Abstract This paper describes operational research (OR) contributions in forestry over the past 50 years, based on scientific pathways along which the authors have traveled. We draw on our personal experiences and recall how the use of OR in forestry has evolved from the early use of linear programming in the Canadian forest products industry in the 1950s and strategic forest management planning by the U.S. Forest Service in the 1960s. We describe the widespread use of OR in many aspects of forestry over a 50‐year timespan (1970–2020) and to the present day, where climate change and biodiversity challenges and increased data availability are important. The paper covers many areas of forestry, including forest management, natural disturbance processes, tactical and operational harvesting, transportation, and value chain management. Each section in the paper includes a historical description of OR‐based key applications as well as OR‐based model and method developments Additionally, we discuss our perceptions of OR in future use and its importance in forestry.
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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 | 0.028 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".