Application of herbal probiotics in feed on growth and blood profile of elver eels (Anguilla bicolor McClelland, 1844)
Bibliographic record
Abstract
Shortfin eel cultivation in Indonesia has shown significant development in aquaculture. However, various challenges still hinder its successful implementation. This study aims to investigate the impact of herbal probiotics on the growth and blood profile of elver eels (Anguilla bicolor). The research was conducted at UPR Mina Mandiri in the Beutong District of Nagan Raya Regency, from August to October 2022This research was conducted at UPR Mina Mandiri, Beutong District, Nagan Raya Regency, from August to October 2022. A Completely Randomized Design (CRD) with five treatment levels and three replications was employed for this study. The treatments consisted of herbal probiotics added to the eels' feed: 0, 15, 20, 25, and 30 ml/kg. The data obtained from the experiment were subjected to an analysis of variance (ANOVA) to assess the significance of the results. ANOVA test results show that probiotics significantly affect survival rate, absolute weight growth, specific weight rates, feed efficiency, and the elver eel blood profile (hemoglobin, erythrocytes, and leukocytes). (P0.05). Duncan's advanced test revealed significant differences in the growth parameters and blood profiles among the various treatments. Treatment B, which utilized a dosage of 15 ml/kg of feed, displayed the most favorable outcomes. It achieved a survival rate of 93.33%, absolute weight growth of 2.69 grams, a specific growth rate of 1.73%, and a feed efficiency of 48.56%. Additionally, the blood profile measurements for treatment B were as follows: hemoglobin levels ranged from 9.53 to 9.73 g/dl, erythrocyte count ranged from 1.12 to 1.23 x 103 cells/mm3, and leukocyte count ranged from 120 to 133 x 103 cells/mm3.Keywords:blood profilefish healthgrowthshortfin eels probiotics
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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".