Important Shark and Ray Areas (ISRAs) – Identifying key habitats for sharks, rays, and chimaeras
Bibliographic record
Abstract
Sharks, rays, and chimaeras (hereafter ‘sharks’) face a high risk of extinction. Populations of many species of sharks and rays have declined by over 70–90% in last few decades. Immediate action is required to halt population declines and allow for species recovery. Area-based measures are key for biodiversity conservation but commonly do not focus on sharks, and existing ones often fail to provide them adequate protection. Target 3 of the Kunming-Montreal Global Biodiversity Framework commits nations to conserving 30% of coastal and marine areas, “especially areas of particular importance for biodiversity”. With that in mind, the Important Shark and Ray Areas (ISRA) approach was developed to ensure sharks are considered and represented in conservation planning approaches. Since 2022, over 600 ISRAs have been delineated across six regions of the world. Analysis have now been undertaken to understand overlap with existing marine protected areas (MPAs), how ISRAs can be integrated into Key Biodiversity Areas (KBAs), and what fisheries management tools can be used at the site level. In several regions (e.g., Central and South American Pacific and Western Indian Ocean), existing MPAs overlap by less than 7% with ISRAs, highlighting poor MPA coverage for these species. Without management measures to reduce fisheries mortality, preventing further losses and species recovery will not be possible. As nations move towards achieving the 30x30 targets by expanding MPA coverage, it is critical that sharks and their important habitats are considered and incorporated in national marine spatial planning processes, guided by robust scientific data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".