Prioritization of public and private land to protect species at risk habitat
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 Conservation budgets are limited, requiring strategic prioritization among actions to efficiently protect species. Systematic prioritization approaches typically determine locations for conservation that most effectively balance species protection with cost. Proxies for cost are frequently used in prioritizing land for protection. Here, we combine financial cost estimates for private land acquisition and species habitat models into a spatial prioritization to explore cost‐effective habitat protection, using a case study of species at risk in Ontario, Canada. Our findings suggest a key trade‐off, whereby protecting the areas with the greatest concentration of species at risk may not be the best strategy for protecting these species. Instead, protecting species at risk may be most cost effective in areas where species‐at‐risk richness is still relatively high, but land costs are relatively low, such as in central Ontario. However, the budget required to adequately protect species at risk through land purchase would be much larger than is currently available for conservation efforts, even if public lands are preferentially protected. Therefore, to effectively protect all species at risk in Ontario, we recommend the use of alternative conservation measures, such as easements and incentives for restoration on private land, to supplement already protected areas.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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