Applying the Key Biodiversity Area Standard to Important Sites for Sharks
Bibliographic record
Abstract
ABSTRACT The Kunming‐Montreal Global Biodiversity Framework commits nations to conserving 30% of coastal and marine areas, “especially areas of particular importance for biodiversity.” Key Biodiversity Areas (KBAs) provide a standardized approach for recognizing sites holding a significant proportion of the global population or extent of species or ecosystems. However, concerns about the relevance of this approach for broadly distributed and/or highly mobile aquatic vertebrates prompted development of parallel approaches focused on critical areas for life‐history processes, including Important Shark and Ray Areas (ISRAs). We examine these approaches and assess whether important areas for sharks, rays, and chimaeras (“sharks”) can qualify as KBAs, by applying the KBA criteria to ISRAs. One fifth of ISRAs could be recognized as KBAs. KBAs could be recognized for three quarters of globally threatened and two thirds of non‐threatened restricted‐range sharks based on published range maps. For broadly distributed species, additional information (e.g., on aggregations) is needed to recognize important sites as KBAs. Our results show that these approaches are complementary, highlighting the potential for ISRAs to contribute to KBA assessments while ensuring important sites for sharks are mapped and available to inform government actions to meet global commitments for conserving biodiversity in coastal and marine areas.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".