The Marine Fisheries Environment of Sierra Leone : Belated Proceedings of a National Seminar Held in Freetown, 25-29 November 1991
Bibliographic record
Abstract
The contribution in this Fisheries Centre Research Report partly explains why Sierra Leone is losing out, and provides information that would support various initiatives currently being undertaken in Africa to recapture access to the resources for the people. A recent effort by the African Confederation of Artisanal Fishing Organizations (CAOPA) is a good example of such new initiative. Recently, CAOPA voiced its demands to FAO and its members, which included the need to (i) document better the impacts of the various types of exploitation of small pelagic fishes on food security; (ii) recommend to states and regional fisheries organizations to consider the role of small pelagic fishes in the ecosystems and in food security of developing countries populations when they are to make decisions for managing these resources, and allocating access to them; (iii) support initiatives and efforts that will contribute to establishing a concerted management of small pelagic resources in West Africa; (iv) support efforts by fishing communities to actively contribute to the management of these resources in a concerted and sustainable way; and (v) support an aquaculture based on species that do not require feed made from wild fish, that answers to the demands of local and regional markets, and that is not contributing to the unsustainable exploitation of small pelagic stocks. Thus: there is a path out of the present arrangements, and it would benefit most people.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".