Replacement of fish meal by earthworm meal (Eiseniafoetida) in Siberian Sturgeon (Acipenserbearii) diet and its effect on growth performance, feed efficiency and carcass composition
Bibliographic record
Abstract
A 12-week feeding trial was carried out to assess the effect of fish meal replacement by earthworm meal (Eiseniafoetida) on growth performance, feed efficiency and carcass composition of Siberian sturgeon (Acipenserbaerii). Fish with mean weight 21.32 ± 1.91 (mean ± SD) were fed with five experimental diets which 0% (Control), 10% (EWM10), 20% (EWM20), 30% (EWM30) and 40% (EWM40) of fish meal protein was replaced by earthworm meal protein. At the end of the feeding trial final body weight, weight gain, condition factor, feed coefficient ratio (FCR) and specific growth rate (SGR) were monitored. Fish fed EWM10 and EWM20 had highest weight gain and final weight respectively but no significant differences were observed between different treatments (P>0.05). Fish fed EWM40 had lowest final weight and weight gain and significant differences were observed between this treatment and EWM10 and EWM20 (P<0.05). The whole carcass protein and ash content were not influenced by earthworm meal replacement and significant differences were not observed between different treatments (P>0.05). Fish fed EWM30 showed lowest carcass lipid content that was significantly lower than control treatment (P< 0.05). This treatment also had highest moisture content and was significantly higher than control treatment (P<0.05). The results of the present study showed that inclusion of earthworm meal in diets can improve growth performance and feed efficiency. So 10 to 20% of fish meal can be replaced with earthworm meal without negative effects on growth performance of the Siberian sturgeon.
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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 | 0.001 |
| 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".