Modeling the Effects of Water Temperature on Growth Rates, Gastric Evacuation and the Return of Appetite in Juvenile Nile Tilapia, Oreochromis niloticus L.
Bibliographic record
Abstract
Optimized aquafeeds have long been a major concern of the sustainable aquaculture development. Not only should the feed composition meet the nutritional requirements of the fish, it should also be reasonably managed (feed ration and feeding frequency) to enhance the feed utilisation efficiency, growth performance and decrease the amount of wastes. At present there is no detailed information on how rearing temperature impacts gastric evacuation rate, return of appetite (RA) and daily feed ration among tilapias, considered as one of the leading fish species for worldwide aquaculture production. The objective of this study was to develope mathematical models to estimate maximum daily feed intake for Nile tilapia in relation to feeding frequency and water temperature. Growth was measured in 480 fish (initial body mass 4.30±0.02 g) fed in slight excess, following their exposure to four thermal treatment (22, 26, 30 and 34 °C) (four replications per treatment, 21-days rearing period, growth monitoring at 5-days intervals). Gastric evacuation and return of appetite measurement were made by radiographic technique. A growth model was developed using a stepwise multiple-regression analysis against fish body mass and water temperature (r2 = 0.939, df = 15) as follow: SGR (%M/day) = -70.606 + 98.433 Log T° – 33.762 (Log T°)2 - 0.153 Log M (Log T°)2. The gastric emptying was described by an exponential function, which was found to be inversely related to the RA. The instantaneous evacuation rates (Re) determined by linearizing the data were strongly affected by rearing temperature (Q10 = 0.047) in Nile tilapia. The RA following a satiation meal was also significantly dependent on rearing temperature. Based on these data, the maximum daily feed consumption was estimated in relation to feeding schedule for juvenile tilapia reared at different temperatures. Considering that unsuitable use of feed adversely impacts on the farm revenue and profit, these results contribute to improve feed management strategies.
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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".