The key literature of, and trends in, forest-level management planning in North America, 1950–2001
Bibliographic record
Abstract
SUMMARY An increase in the use of operations research techniques for forest-level planning, as expressed by publication of papers in peer-reviewed North American forestry journals, is illustrated by the number of papers published that describe a mathematical problem formulation, or model used, and demonstrates an application of the planning process. A shift in planning from a dependence on linear programming to heuristics is evidenced through the literature review, although linear programming and its derivatives continue to be used to demonstrate the development of strategic forest plans, plans without spatial components, or relaxed solutions to more complex forest planning problems. Initially, wood production and economic goals dominated the themes of journal articles, but just as the forest management environment has evolved to include an explicit recognition of non-timber goals, so have mathematical programming techniques evolved to support the development of forest plans with non-timber goals. Spatial components within forest planning processes have also increased dramatically in the last decade, as resource goals that key off of the juxtaposition of activities have become increasingly important. Finally, two North American forestry journals, the Canadian Journal of Forest Research and Forest Science, have become the predominant sources of forest-level planning literature that focuses on forest planning problem formulations and examples of the use of mathematical programming techniques in forest-level planning.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".