Is there a need for a ‘100 questions exercise’ to enhance fisheries and aquatic conservation, policy, management and research? Lessons from a global 100 questions exercise on conservation of biodiversity
Bibliographic record
Abstract
Recent global and regional exercises have been undertaken to identify 100 questions of relevance to policy makers that, if answered, would improve decision making and conservation actions. These were intentionally broad, but all included themes and questions of relevance to aquatic and fisheries professionals (e.g. exploitation, habitat alteration, effectiveness of protected areas, migratory connectivity and environmental effects of aquaculture). Here, the content of the global 100 question exercise relevant to aquatic and fisheries issues is summarized and a critical analysis is provided. Many of the questions addressed in apparently unrelated themes and topics (e.g. terrestrial, agriculture and energy policy) have potential relevance to fisheries and aquatic habitats, which underlines the connectivity between terrestrial and aquatic realms. Given the intimate link between aquatic environmental problems and human activities (including culture and economics), greater understanding of the human dimension is required to inform decision making. Stakeholder perspectives need to be included as a core component of the fisheries management triangle (i.e. managing fish, habitat and people). The benefits and risks of conducting a global 100 questions exercise with an exclusive focus on questions of relevance to fisheries and aquatic practitioners are also considered. There is no question that evidence-based approaches to conservation are essential for addressing the many threats that face aquatic ecosystems and reverse the imperilment trends among ichthyofauna. It is still unclear, however, as to the extent to which 100 questions exercises will help to achieve conservation and management targets for aquatic resources. A global 100 questions exercise that focused on fisheries and aquatic issues would certainly help to generate interest and awareness sufficient to justify such an exercise.
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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 | 0.124 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.029 | 0.055 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".