Effect of stocking density on growth and survival of Nile tilapia (Oreochromis niloticus, Linnaeus 1758) under cage culture in Lake Albert, Uganda
Bibliographic record
Abstract
In Uganda fish is a great source of animal dietary protein, however, natural stocks continue to decline. Therefore, aquaculture provides a viable option to bridge the increasing fish supply-demand gap. Accordingly, a study was conducted from March to August 2016 to investigate the effect of stocking density on the growth performance, and survival of Nile tilapia (Oreochromis niloticus) in floating net-cages to contribute to aquaculture production in Uganda. Nile tilapia fingerlings, with an average weight of 4.07 g were stocked in 8 m3 cages at three different stocking densities; 200, 250, and 300 fish/m3, and fed on a locally formulated commercial feed for 180 days. At the end of the experimental period, results showed that fish stocked at lower densities were heavier than those stocked at higher densities. The mean final weights of fish were; 150.79±85.71, 127.82±68.43 and 118.73±49.29 g in cages stocked with 200, 250 and 300 fish/m3, respectively. The mean final weight in lower density (200 fish /m3) treatments was significantly higher (P<0.05) than that of higher density (300 fish/m3) treatments. The mean relative condition factor of fish ranged from 1.02 to 1.06, but was not significantly different (P>0.05) among the stocking densities. Survival rate and stocking density were negatively correlated. The best survival rate (94.19%) was obtained in low stocked cages (200 fish/m3) compared to 92.98% in highly stocked cages (300 fish/m3). The results of this study suggest that 200 fish/m3 of a cage, is the best stocking density in terms of fish growth parameters. Key words: Aquaculture, animal protein, per capita, livelihood, yield.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".