Opportunities to enhance conservation success for sharks
Bibliographic record
Abstract
Sharks, rays, and chimaeras are among the most threatened vertebrate lineages. Despite considerable conservation efforts, the extinction risk of sharks continues to rise. We present a quantitative analysis of the shark conservation literature, exploring trends and interconnectivities in key topics using a machine learning approach. We show that shark conservation research is a well interconnected, coherently structured, and rapidly expanding field centred around a conservation nexus linking human-wildlife interactions to species use and management. Shark conservation research is increasingly interdisciplinary and is well prioritised toward key threats that drive the decline of shark populations, both of which are key to effective management. However, we also identify opportunities to further strengthen research and management. These include improved integration of key research topics, enhancing the understanding of combined threats, and greater consideration for the role of sub-lethal impacts. Lastly, we stress that meaningful integration of research topics, rather than simple contextualisation, is essential to building the comprehensive and nuanced understanding necessary to inform effective conservation actions. By leveraging the strengths of the field and addressing its remaining weaknesses, there is hope for a future where sharks thrive and contribute to healthy, resilient marine ecosystems.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".