A multi criteria-Choquet based forest resources-allocation mechanism
Bibliographic record
Abstract
We design a mechanism that considers multiple criteria aspects to allocate a limited amount of public owned forest resources to competing mills. Our approach considers different evaluations of the mill through its performance according to the three sustainability criteria (economic, environmental and social aspects) and its contribution to the collaborative efforts in coalitions of mills, which is the fourth criterion. Moreover, collaboration is considered along three forest operations, where each mill can belong to more than one coalition at the same time (overlapping coalitions). The coalition configuration value (CCV) is applied to evaluate the fourth criterion in a regional case study in the province of Quebec (Canada). The four criteria are assumed to be dependent and influence each other. This is why, we propose the use of the Choquet Integral as an aggregation function of the four criteria into a single value that takes into account the interactions among the criteria. This aggregated value is used as a unique performance metric of the mills in an optimization model which allocates the wood volumes to the mills proportionally to this performance. When the fourth criteria is calculated by the CCV, the allocation is said to be Choquet-CCV based. We also compare this allocation with the Choquet-least-core-value based where the least core value is used as a measure of the fourth criterion. We also compare these allocations with those obtained from a previous work.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".