Envisioning 2050: climate change, aquaculture and fisheries in West Africa. Dakar, Senegal 14-16 April 2010
Bibliographic record
Abstract
This report presents the activities and results of the workshop Envisioning 2050: Climate Change, Aquaculture and Fisheries in West Africa. The objectives of the workshop were to discuss critical issues and uncertainties faced by the fisheries and aquaculture sector in Ghana, Senegal and Mauritania, build sectoral scenarios for 2050 and discuss the implication of these scenarios in the context of climate change for the countries and the region. During the workshop participants were introduced to scenario- building methodologies, identified drivers of change and ranked them according to their importance and levels of uncertainty. Participants then constructed four consistent scenarios for 2050 for each country. The scenarios raised several questions including: Can aquaculture address both national food security and macroeconomic growth? Should regional trade be promoted or access to global markets prioritised? How will climate change affect fishery resources, especially small pelagic fish like sardines, which are an important export commodity for Senegal and Mauritania? Participants also assessed the implications of the different scenarios in terms of climate change and research and development in the fisheries and aquaculture sector. It was agreed that a regional or sub-regional effort is needed to better integrate scientific knowledge and to put into place coherent fisheries policies. Additionally, a better understanding of the impacts of climate change on the sector is needed, with for instance, the development of coupled climate-fisheries models for major commercial fisheries in the region. Participants unanimously agreed that strategic planning and foresight studies methodologies should be widely disseminated. Indeed the opportunity for reflective and creative thinking was recognised as an important part of planning - especially adaptation planning - to climate change. Finally, the workshop provided a rare opportunity to include in a foresight study, art projects by youth on the future of the fisheries sector and the coasts as an exhibition and discussion theme (“Visions of the Future: What is African Youth telling us about our Ocean?”). Empowering youth on climate change issues and integrating their needs into adaptation planning is essential as they will be the ones most affected by future developments.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".