Ten research questions to support South Africa’s Inland Fisheries Policy
Bibliographic record
Abstract
South Africa is in the process of developing a National Freshwater (Inland) Wild Capture Fisheries Policy. A properly focused research strategy is essential to guide the policy development process, and thus a dedicated ‘Inland Fisheries’ workshop was convened by the South African Society for Aquatic Scientists in June 2018 to update and further develop a list of priority knowledge requirements for inland fisheries in the country. The main themes that emerged during the workshop were developed and contextualised as ten research questions. These were: (1) What is the exploitation potential of inland fisheries? (2) What are the health risks from consuming freshwater fishes? (3) Who currently uses inland fisheries and what are their harvests? (4) What can we learn from historical constraints to inland fisheries development? (5) How will governance of fisheries have to change in an evolving sectoral environment? (6) What are the options for fisheries enhancement? (7) What are the most appropriate fisheries technologies? (8) What value chains and employment opportunities are associated with inland fisheries? (9) What is the impact of water level fluctuations on fish production? (10) What are the impacts of pathogenic diseases on fish populations?
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 | 0.055 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 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".