Feeding ecology of the intensively fished Nile Perch, <i>Lates niloticus,</i> in Lake Victoria, Uganda
Bibliographic record
Abstract
The diet of Nile Perch (8.0–121.0 cm total length [TL]) from the Ugandan waters of Lake Victoria was quantified through stomach content analysis of specimens collected from experimental catches and fish factory samples. A total of 7824 stomachs (5602 from experimental fishing and 2222 from factory samples) were examined, of which 34.8% contained food. Fish from the experimental catches were smaller (8.0–41.6 cm TL) and had a higher diversity of prey dominated by unidentifiable fish prey, haplochromine cichlids, Rastrineobola argentea, Odonata and Caridina nilotica, while larger fish (30.0–121.0 cm TL) from the factory samples had a predominance of fish remains and haplochromine cichlids. Nile Perch that had a high proportion of fish prey (versus invertebrates) in their stomachs showed a larger size for a given age, and were in a better condition (K = 1.24) than those that had primarily invertebrates (K = 1.10) in their stomachs. Nile Perch exhibited a much smaller size (15 cm versus 30 cm TL) at shift to piscivory in comparison to Nile Perch examined in earlier studies, when haplochromines were rare in Lake Victoria. The recovery of haplochromine cichlids coincident with declining Nile Perch densities illustrates the importance of developing sustainable management options that can define a proper balance between fishing mortality and Nile Perch predation.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".