Using preference information in developing alternative forest plans
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
The development of new alternative plans based on applying multicriteria decision making (MCDM) techniques in discrete choice situations has received little attention in the context of forest planning. This article proposes a two-stage approach to be applied in participatory decision-making situations in which a specific number of initial alternatives are evaluated by the decision makers (DMs) using MCDM analysis. The preference information, obtained from these analyses in the form of target values, is then used for generating still more efficient forest plans. This paper concentrates on the latter stage and tests nine different goal programming (GP) formulations. This paper uses the formulas and preference information obtained from a case study of three forest owners to generate new forest plans. Among the tested techniques, formulas with a penalty function provided the most appropriate plans. These GP formulations could enhance the participatory planning processes in which a discrete number of alternatives are evaluated. With further development, this process could be applied to a variety of forest ownership types and could be a useful tool in supporting group decision making. This proposed approach could facilitate an increase in the DMs’ satisfaction and an increased commitment towards the derived decision.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it