The Effects of Kefir Usage for Probiotic Purpose on Growth Performance of Brook Trout (Salvelinus fontinalis)
Bibliographic record
Abstract
The effects of probiotically used kefir on growth, survival rate and meat yield characteristics of brook trout (Salvelinus fontinalis) were investigated in this study. For this purpose, kefir was added to trial diets at different rates 20 ml/kg feed (G2), 40 ml/kg feed (G3) while commercial fish feed was used as a control 0 ml/kg feed (G1). Total 270 fish with mean weight 24.38±0.37 g were used during the experiment. Each group had three replicate, and 30 fish were placed in each tank. Experiment was carried on for 90 days. At the end of the experiment, some growth parameters, survival rate and meat yields of the groups were determined. The best weight gain (WG), specific growth rate (SGR), feed conversion ratio (FCR) and survival rate (SR) values were obtained in G2 (20 ml/kg) group (104.31±0.11, 1.30±0.08, 1.40±0.18, 98.88±0.51 respectively). At the end of the study, the differences between the control group and the other groups were not statistically significant in terms of FCR, Condition factor (CF) and survival rate (P < 0.05). However, the differences between the control group and the other groups were statistically significant in terms of WG, WGR and Wf (P < 0.05). The statistical difference between the G2 group and the G1 group was not significant at the SGR, whereas the statistical difference between the G2 group and the G3 group was significant (P < 0.05). The body composition of the fish, hepatosomatic index (HSI), viscerosomatic index (VSI) values, fish meat crude protein and crude fat content were not affected by the addition of kefir to fish diet at different ratios. As a result, it was observed that the addition of kefir at 20% of the diet positively affected the growth performance and survival rate of the brook trout.
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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) | 0.000 | 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".