Enrichment of Artemia franciscana with soybean-lecithin and its beneficial effect on biochemical composition of broodstocks and fatty acids composition of eggs in Cichlid Green Terror (Aequidens rivulatus)
Bibliographic record
Abstract
Abstract In the current study, the performance of soybean lecithin-enriched adult Artemia franciscana and its beneficial effects as a replacement for commercial diet were evaluated by determining the biochemical and fatty acid composition of broodstocks and eggs of green terror cichlid (Aequidens rivulatus) for 90 days. Eight hundred and ten fish (3.1 ± 0.2 g) were randomly allotted into glass aquaria (80 L) and assigned to ten dietary treatments at five different replacement levels (0, 25, 50, 75, and 100%) of the commercial diet (CD) with either un-enriched Artemia (UA) or lecithin-enriched Artemia (EA). Based on the results, enrichment of Adult Artemia with soy lecithin increased body lipid content in 25% EN, 50% EN and 75% EN treatments, although there was no significant difference between dry matter, crude protein and ash between the diet groups (P < 0.05). The highest level of total polar lipid (18.26%) was observed in broodstocks of Green Terror fed 50CD: 50EA. Based on fatty acid composition, the highest amount of saturated fatty acids of broodstocks of Green Terror was revealed in 75CD: 25UA and 50CD: 50UA treatments. The lowest amount of monounsaturated fatty acids was observed in the 50CD: 50EA treatment. The highest amount of DHA (17.81%) was observed in 25CD: 75EAtreatment. The fatty acid analysis of eggs showed significantly higher SFA and lower MUFA in 75CD: 25EA treatment. Furthermore, the PUFA and DHA level exhibit significantly higher in 50CD: 50EA treatment. In conclusion, the 50CD: 50EA diet improved the lipid and fatty acid composition of Green Terror cichlid fish and therefore, this feeding strategy can be recommended in upgrading the nutritional management of this species.
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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".