For the sake of resilience and multifunctionality, let's diversify planted forests!
Bibliographic record
Abstract
Abstract As of 2020, the world has an estimated 290 million ha of planted forests and this number is continuously increasing. Of these, 131 million ha are monospecific planted forests under intensive management. Although monospecific planted forests are important in providing timber, they harbor less biodiversity and are potentially more susceptible to disturbances than natural or diverse planted forests. Here, we point out the increasing scientific evidence for increased resilience and ecosystem service provision of functionally and species diverse planted forests (hereafter referred to as diverse planted forests) compared to monospecific ones. Furthermore, we propose five concrete steps to foster the adoption of diverse planted forests: (1) improve awareness of benefits and practical options of diverse planted forests among land‐owners, managers, and investors; (2) incentivize tree species diversity in public funding of afforestation and programs to diversify current maladapted planted forests of low diversity; (3) develop new wood‐based products that can be derived from many different tree species not yet in use; (4) invest in research to assess landscape benefits of diverse planted forests for functional connectivity and resilience to global‐change threats; and (5) improve the evidence base on diverse planted forests, in particular in currently under‐represented regions, where new options could be tested.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".