Use of agriculture by-products (brans and meal) as food for Artemia franciscana (Kellogg, 1906) and effects on performance and biochemical compositions
Bibliographic record
Abstract
Aquaculture needs to provide live food such as Artemia and one of the most important issues in A. franciscana rearing, is a food supply. The instar I nauplii were fed in seven treatments including Wheat bran, Rice bran, Soy-meal and algae (Dunaliella salina). For each treatment calculated growth rate, survival percentage, and body composition. At the reproductive period, 35 pairs were individually isolated from each treatment and transferred to 50 mL Falcon tubes in which the reproduction of females were monitored until their deaths. The result showed that in Artemia fed by the agricultural by-product, rice bran (10.71±0.80) and wheat bran (10.82±0.32) obtained the highest growth after control group (12.93±0.16), and the highest survival observed in control (56.00±1.76). Most of the offspring were observed in control (896.83±50.27) and wheat bran (880.37±43.88), but there was no statistical difference between them. Although A. franciscana was fed with rice bran similar to the control group in terms of body protein, Artemia was fed with wheat bran, significantly increased in body protein. According to the results of this study, it can be concluded that using wheat -and rice bran could be replaced about 90 percent of algae and kept the quality of culturing A. franciscana at best.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".