Trade-off analysis for decision making in natural resources: Where we are and where we are headed
Bibliographic record
Abstract
Forest management involves making trade-offs to balance social, ecological, and economic objectives of the forest. In the past this decision making was primarily done by trained professionals. Forest certification requires a greater involvement by the public, and has created a need for formal methods to make trade-offs in a transparent and balanced manner. This paper explores the nature of trade-offs in the historical context of forest management in British Columbia. It describes the development of forest management in the context of ecosystem and intergenerational trade-offs that have been made which are often in conflict with the public's value system. The difficulties in using public preferences to make decisions are discussed, and the available methods used for conducting trade-off analysis in forest management are critically reviewed. The author recommends a set of guidelines for public participation that are learning-based and designed to build public confidence in the decision-making process. A continuous improvement approach for implementing management decisions is also recommended. Research needs to provide supporting tools for sustainable forest management planning are described.
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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 | 0.044 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".