Incorporation of preferential uncertainty into interval-scale priority functions — a case of multicriteria forestry decision making
Bibliographic record
Abstract
Quantitative decision analysis and respective planning models offer many benefits in forest planning. They are efficient, quick, inexpensive, objective, and repeatable. However, quantitative planning and the respective planning models also include many sources of uncertainty. In this paper our research objective is to analyse decision makers’ preferential uncertainty in the context of multicriteria forestry decision making by using statistical models for so-called interval-scale continuous decision problems. The models are based on 0–1-type utility functions by applying a Finnish school grading system for collecting the preference data. The basic idea is to assess the preferences at some discrete points and then estimate the continuous priority function according to the statistical estimation techniques. An interactive approach for reducing the preferential uncertainty included in the priority model is also developed. In the interactive step, the improvements of the priority model are based on uncertainty measures related to local and (or) global priority models. The role of statistical uncertainty analysis is to make the interactive planning process more efficient and reliable. The interactive and statistical approaches complement each other and promote decision makers’ learning.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".