For the sake of resilience and multifunctionality, let's diversify planted forests!
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it