Using ecological thresholds to evaluate the costs and benefits of set-asides in a biodiversity hotspot
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
Ecological set-asides are a promising strategy for conserving biodiversity in human-modified landscapes; however, landowner participation is often precluded by financial constraints. We assessed the ecological benefits and economic costs of paying landowners to set aside private land for restoration. Benefits were calculated from data on nearly 25,000 captures of Brazilian Atlantic Forest vertebrates, and economic costs were estimated for several restoration scenarios and values of payment for ecosystem services. We show that an annual investment equivalent to 6.5% of what Brazil spends on agricultural subsidies would revert species composition and ecological functions across farmlands to levels found inside protected areas, thereby benefiting local people. Hence, efforts to secure the future of this and other biodiversity hotspots may be cost-effective.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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