Synergistic Microbial Interactions Between Algae and Bacteria Augment Growth and Immune Performance in Red Tilapia (Oreochromis sp.)
Bibliographic record
Abstract
This study investigated the effects of integrating biofloc with microalgae on growth performance and immune gene expression in red tilapia (Oreochromis sp.). The experiment consisted of four treatments: C (Biofloc), T1 (Chlorella vulgaris and Nannochloropsis sp.; 1:1), T2 (Biofloc + Chlorella vulgaris and Nannochloropsis sp.; 1:1), T3 (Biofloc + Chlorella vulgaris and Nannochloropsis sp.; 2:1) in 500 L plastic tanks for 60 days. T2 and T3 exhibited the lowest ammonia and nitrite levels, respectively. T3 exhibited the highest chlorophyll a and chlorophyll b levels, while T2 showed the highest carotenoid content. T2 showed the highest weight gain (142 ± 0.7 g) and SGR (1.61 ± 0.02) and the lowest FCR (1.79 ± 0.009). T2 exhibited the highest gene expression levels in the intestine, with 7.8-fold upregulation of the cathepsin L (ctsl) gene, 3-fold upregulation of toll-like receptor 7 (tlr7), 6.7-fold upregulation of interleukin-1 b (il-1b), 4.7-fold upregulation of tumor necrosis factor-alpha (tnf-a), and 2.8-fold upregulation of metallothionein (mt). In the head kidney, the mt upregulation was highest in T3 (7.2-fold), while tnf-a and tlr7 upregulations were highest in T2 (5.9-fold and 5-fold, respectively). In the liver, the gene expressions were highest in T3, with 6.4-fold upregulation of mt, 5-fold upregulation of ctsl, 2.7-fold upregulation of tlr7, 3-fold upregulation of il-1b, and 5.4-fold upregulation of tnf-a. These results suggest a synergistic effect of algae and bacteria on immune and antioxidative capacity in red tilapia.
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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".