Fishing down and fishing hard: ecological change in the Nile perch of Lake Nabugabo, Uganda
Bibliographic record
Abstract
Abstract – Fishing is a potent ecological force. In Lake Victoria, East Africa, Nile perch, Lates niloticus contributes to a multi‐million dollar fishing industry but is threatened by over‐exploitation. We quantified spatial and temporal trends in the distribution, diet and size of Nile perch in Lake Nabugabo, Uganda, a satellite of Lake Victoria. From 1995 to 2007, we detected a decline in catch per unit effort of Nile perch, a shift in their distribution and diet, and a decrease in their body size. A greater proportion of Nile perch were found near wetland ecotones than in the 1990s. This may reflect intensive size‐selective fishing in open waters, and encroachment of Vossia cuspidata, an emergent macrophyte that has expanded across the lakeshore. Results highlight the strength of fishing in inducing phenotypic changes in target stocks as well as large‐scale changes to the aquatic community and are of value in understanding changes in Lake Victoria.
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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 | 0.000 | 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".