Fifty years of operational research in forestry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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 it