Prioritizing species conservation programs based on IUCN Green Status and estimates of cost‐sharing potential
Bibliographic record
Abstract
Over 1 million species around the world are at risk of extinction, and conservation organizations have to decide where to invest their limited resources. Cost-effectiveness can be increased by leveraging funding opportunities and increasing collaborative partnerships to achieve shared conservation goals. We devised a structured decision-making framework to prioritize species' conservation programs based on a cost-benefit analysis that takes collaborative opportunities into account in an examination of national and global conservation return on investment. Conservation benefit is determined by modifying the novel International Union for the Conservation of Nature Green Status for Species to provide an efficient, high-level measure that is comparable among species, even with limited information and time constraints. We applied this prioritization approach to the Wilder Institute/Calgary Zoo, Canada, a nonprofit organization seeking to increase the number of species it assists with conservation translocations. We sought to identify and prioritize additional species' programs for which conservation translocation expertise and actions could make the most impact. Estimating the likelihood of cost-sharing potential enabled total program cost to be distinguished from costs specific to the organization. Comparing a benefit-to-cost ratio on different geographic scales allowed decision makers to weigh alternative options for investing in new species' programs in a transparent and effective manner. Our innovative analysis aligns with general conservation planning frameworks and can be adapted for any organization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".