The importance of research and public opinion to conservation management of sharks and rays: a synthesis
Bibliographic record
Abstract
Growing concern for the world’s shark and ray populations is driving the need for greater research to inform conservation management. A change in public perception, from one that we need to protect humans from sharks to one where we must protect sharks from humans, has added to calls for better management. The present paper examines the growing need for research for conservation management of sharks and rays by synthesising information presented in this Special Issue from the 2010 Sharks International Conference and by identifying future research needs, including topics such as taxonomy, life history, population status, spatial ecology, environmental effects, ecosystem role and human impacts. However, this biological and ecological research agenda will not be sufficient to fully secure conservation management. There is also a need for research to inform social and economic sustainability. Effective conservation management will be achieved by setting clear priorities for research with the aid of stakeholders, implementing well designed research projects, building the capacity for research, and clearly communicating the results to stakeholders. If this can be achieved, it will assure a future for this iconic group, the ecosystems in which they occur and the human communities that rely on them.
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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 | 0.054 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".