The importance of research and public opinion to conservation management of sharks and rays: a synthesis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 it