Designing optimal human‐modified landscapes for forest biodiversity conservation
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
Agriculture and development transform forest ecosystems to human-modified landscapes. Decades of research in ecology have generated myriad concepts for the appropriate management of these landscapes. Yet, these concepts are often contradictory and apply at different spatial scales, making the design of biodiversity-friendly landscapes challenging. Here, we combine concepts with empirical support to design optimal landscape scenarios for forest-dwelling species. The supported concepts indicate that appropriately sized landscapes should contain ≥ 40% forest cover, although higher percentages are likely needed in the tropics. Forest cover should be configured with c. 10% in a very large forest patch, and the remaining 30% in many evenly dispersed smaller patches and semi-natural treed elements (e.g. vegetation corridors). Importantly, the patches should be embedded in a high-quality matrix. The proposed landscape scenarios represent an optimal compromise between delivery of goods and services to humans and preserving most forest wildlife, and can therefore guide forest preservation and restoration strategies.
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) | 0.001 | 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 | 0.001 |
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 it