Planning for Biodiversity Conservation Using Stochastic Programming
Why this work is in the frame
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Bibliographic record
Abstract
SummaryRapid species extinctions and the loss of other biodiversity features worldwide have prompted the development of a systematic planning framework for the conservation of biodiversity. Limited resources (~ 40 million USDannually) are available for conservation, particularly in the developing countries that contain many of the world’s hotspots of species diversity. Thus, conservation planning problems are often represented as mathematical programs in which the objective is to select sites to serve as conservation areas so that the cost of the plan is as small as possible and adequate habitat is protected for each species. Here, we generalize this approach to allow for uncertainty in the planning process. In particular, we assume that the species to be protected disperse after the conservation areas are established and that planners cannot anticipate with certainty the species’ future locations when selecting the conservation areas. This uncertainty is modeled by including random variables in the mathematical program. We illustrate the approach by designing a network of conservation areas for birds in southern Quebec.
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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) | 0.001 | 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 | 0.000 |
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