Looking Beyond the Fenceline: Assessing Protection Gaps for the World's Rivers
Bibliographic record
Abstract
Abstract Protected areas are a cornerstone strategy for terrestrial and increasingly marine biodiversity conservation, but their use for conserving inland waters has received comparatively scant attention. In 2010, the Convention on Biological Diversity (CBD) included a target of 17% protection for inland waters, yet there has been no meaningful way of measuring progress toward that target. Defining and evaluating “protection” is especially complicated for rivers because their integrity is intimately linked to impacts in their upstream catchments. A new generation of global hydrographic data now enables a high‐resolution, standardized assessment of how upland activities may be propagated downstream. Here, we develop and apply, globally, a river protection metric that integrates both local and upstream catchment protection. We found that “integrated” river protection is highly variable across geographies and river size classes and in most basins falls short of the 17% CBD target. Around the world, about 70% of river reaches (by length) have no protected areas in their upstream catchments, and only 11.1% (by length) achieve full integrated protection. The average level of integrated protection is 13.5% globally, yet the majority of the world's largest basins show averages below 10%. Within basins, gaps are particularly severe for larger rivers.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".