Effects of Artemia nauplii enrichment with a bacterial species (Weissiella koreensis) on growth performance and survival rate of stellate sturgeon larvae (Acipenser stellatus)
Bibliographic record
Abstract
Artemia franciscana nauplii was enriched with a bacterial species Weissiella koreensis (lactic acid gram-positive) isolated from the alimentary tract of Stellate sturgeon (Acipenser stellatus), at 3 levels. Treatments included T1: 6.9×104 colony-forming units (CFU) mL-1, T2: 6.9×105 CFU mL-1, T3: 6.9×106 CFU mL-1 and their effects were compared with the control diet (no probiotic were added). Each diet was fed to triplicate tanks. In total 3600 larvae were distributed in 12 fiberglass tanks (300 larvae per each tank contain 100 L of water). The larvae were fed, 60% of the body weight per day, with enriched Artemia nauplii, immediately after absorption of yolk sac, 6 times a day for 14 days. The results showed that, enrichment of Artemia nauplii with Weissiella koreensis in the T1, T2 and T3 could significantly enhance feed conversion ratio (FCR), in comparison with control (P<0.05). Survival in T3 and T1 was significantly more than control (P<0.05). Also biomass increase, in T3 was significantly more than control (P<0.05). It can be stated that, the T3, in term of effect on growth performance was in a more favorable condition than others treatments and control.
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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".