The Effects of Different Feeding Rates on Growth Performance and Stomach Volume in Rainbow Trout (Oncorhynchus mykiss)
Bibliographic record
Abstract
In this study, the effects of various daily feeding ratios on the growth, stomach volume and meat composition in rainbow trout (Oncorhynchus mykiss) were investigated. The feeding experiment was conducted in 450 L volume fiberglass tanks with 3 × 3 factorial patterns. During the experiment, while food were given to the fish at the first group in the 0.5% (I) of ratio of their live weight, the second group at the level of 2% (II) food were given to the fish, at the third group at the level of 6% (III) food. The initial weight and size values of the groups were 76.16±0.41 g and 19.11±1.63 cm, respectively. The final weight and size values reached up to 128.89±34.21 g, 25.09±2.37 cm; 236.05±89.32 g, 24.78±2.22 cm; and 238.91±86.67 g, 21.65±1.64 cm, for groups I, II, and III respectively. The best growth performance in terms of weight was obtained in group III, while the best growth performance in terms of size was obtained in groups I and II (P < 0.05). The best feed conversion ratio was determined in the low feeding group I (P < 0.05). At the end of experiment, three of the experimental groups were different from each other. The highest stomach volume was measured in group III (P < 0.05). In conclusion, the 6% feeding ratio increased the growth and significantly increased the stomach volume, however decreased the feed conversion ratio.
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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 | 0.000 | 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".