Improvement of growth and hematological profile of juvenile silver barb (Barbonymus gonionotus) through dietary probiotic (Bacillus sp.) supplementation
Bibliographic record
Abstract
A 75 days feeding trial was conducted to evaluate the influence of dietary supplementation of multi-strain (Bacillus sp.) probiotic (22×109 CFU/g) on growth and hematological parameters of Barbonymus gonionotus. A total 270 uniformed size (length 2.40±0.16 cm and weight 4.25±0.01g) fishes were equally and randomly distributed in nine (three treatments each with three replication) glass aquaria of 200L capacity. The control (T1) group was fed with normal feed (Diet 1) and treatment T2 and T3 were fed a feed containing 1% (Diet 2) and 3% (Diet 3) probiotic respectively. Biometry and blood samples of experimental fishes were collected at the end of the experiment. Significantly (p<0.05) best growth performance such as length gain, weight gain, % weight gain, specific growth rate and best feed utilization parameters such as feed conversion ratio and protein efficiency ratio values were observed in fish fed with 1% probiotic supplemented diet than other treatments. Moreover, higher values in hemoglobin, hematocrit, red blood cell and white blood cell were observed in 1% probiotic treated group (T2). Other hematological indices like mean cell volume, mean cell hemoglobin and mean cell hemoglobin concentration values supported the fact that fish fed with probiotic treated diet were healthier. The result suggests that supplementing diet with 1% probiotic could be effectively used as feed supplement for improving growth and health status of B. gonionotus.
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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".