Marine Fishing Management Towards Sustainability in Sierra Leone
Bibliographic record
Abstract
Achieving global goals of eradicating hunger and poverty before 2030 requires improved resource management. This research analyses the historic use of marine fish in Sierra Leone from 1976 to 2019, captures original data of local market access, profit and waste in 2020, and presents worldwide case studies and a new transferable framework to assist national authorities and managers to increase food security and improve management, achieving related Sustainable Development Goals (SDGs) through policies, technology and economics. Fish catch, export and Catch Per Unit Effort (CPUE) data from FAO FishStatJ, fisheries ministry and secondary sources, and 218 surveys of marketplace fish sellers, were analysed by simple descriptive and comparative statistics. Total fish catch increased substantially in recent decades, while CPUE fluctuates and declined between 1999/2000 and 2010. Many fisheries are exploited or overexploited and market sellers commonly do not have access to enough fish. Exports are consistently low. Case studies in developing and developed countries demonstrate that resolutions are three-pronged: improved awareness of environmental impacts, laws and policing; science and technology utilisation in monitoring resources and fishing activities, and establishing best practice, and; international cooperation, agreements and fair use policies. Priority should be given to unite government and community fishing relationships.
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.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".