Possibility schema for interdisciplinary forest management evaluation and decision-making
Bibliographic record
Abstract
Interdisciplinary planning and evaluation of forest management is necessary for sustainable forest management (SFM) schemes involving multiple values of multi-stakeholders. Often, multi-objective forest-planning and evaluation encounter complexity and uncertainty due to "inexactness"— i.e., fuzziness, ambiguity, imprecision and variability — of spatial behaviours of ecological and human systems. This paper develops the possibility schema — from fuzzy sets and theory of possibility — for representation and evaluation of inexact spatial concepts, configurations, and processes, associated with forest ecosystem and stakeholder values. A hypothetical case of interdisciplinary research utilizing criteria and indicators of SFM is used to illustrate the utility of the proposed possibility schema in interdisciplinary forest decision-making. The schema can be used for ex-ante appraisal and ex-post evaluation of forest programs. It can also be used for integration of interdisciplinary forest knowledge, including ecological and socio-economic models of SFM. Key words: decision-making, fuzzy sets, inexactness, interdisciplinary evaluation, multiple values, possibility theory, sustainable forest management, uncertainty
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".