Roadmap to develop a stress test for forest ecosystem services supply
Bibliographic record
Abstract
Forests play a key role in a bio-based economy by providing renewable materials, mitigating climate change, and accommodating biodiversity. However, forests experience massive increases in stresses in their ecological and socioeconomic environments, threatening forest ecosystem services supply. Alleviating those stresses is hampered by conflicting and disconnected governance arrangements, competing interests and claims, and rapid changes in technology and social demands. Identifying which stresses threaten forest ecosystem services supply and which factors hamper their alleviation requires stakeholders' perceptions. Stakeholder-oriented stress tests for the supply of forest ecosystem services are therefore necessary but are not yet available. This perspective presents a roadmap to develop a stress test tailored to multiple stakeholders' needs and demands across spatial scales. We provide the Cascade and Resilience Rosetta , with accompanying performance- and resilience indicators, as tools to facilitate development of the stress test. The application of the stress test will facilitate the transition toward a bio-based economy in which healthy and diverse forests provide sustainable and resilient ecosystem services.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".