Balancing equity and efficiency of goal programming for use in forest management planning
Bibliographic record
Abstract
Developing forest management plans from a multicriteria perspective requires the decision maker to state preferences regarding the criteria and their importance. This article demonstrates the feasibility of merging the weighted goal programming formulation and the minimax goal programming formulation as a means to provide the decision maker the opportunity to decide which goals are to be treated in a weighted goal programming (the efficient solution) manner or in a minimax goal programming (the equitable solution) manner. The combination of these two goal planning variants is done through partitioning the criteria into sets to be treated by the different variants. The two methods proposed in this paper assign different criteria to a set where the balance of the achievements is desired or to a different set where the maximum aggregated achievement is desired. The first method is designed to create alternative plans, based only on the assignment of criteria to different partitions. The second method requires that the decision maker has a clear understanding of how he/she wishes to deal with each criterion. The functionality of these methods of goal planning is shown with an example derived from a forest management planning situation.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".