Data from: Targeting global conservation funding to limit immediate biodiversity declines
Bibliographic record
Abstract
Inadequate funding levels are a major impediment to effective global biodiversity conservation and are likely associated with recent failures to meet United Nations biodiversity targets. Some countries are more severely underfunded than others and therefore represent urgent financial priorities. However, attempts to identify these highly underfunded countries have been hampered for decades by poor and incomplete data on actual spending, coupled with uncertainty and lack of consensus over the relative size of spending gaps. Here, we assemble a global database of annual conservation spending. We then develop a statistical model that explains 86% of variation in conservation expenditures, and use this to identify countries where funding is robustly below expected levels. The 40 most severely underfunded countries contain 32% of all threatened mammalian diversity and include neighbors in some of the world’s most biodiversity-rich areas (Sundaland, Wallacea, and Near Oceania). However, very modest increases in international assistance would achieve a large improvement in the relative adequacy of global conservation finance. Our results could therefore be quickly applied to limit immediate biodiversity losses at relatively little cost.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.023 |
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".