Effects of replacing fish oil with stearine as main lipid source in diet on growth and survival of Pacific White Shrimp, Litopenaeus vannamei (Boone, 1931)
Bibliographic record
Abstract
The stearine fraction from raw fish oil refinement has been treated as a waste material. This study was conducted to evaluate effects of replacing prime refined fish oil with stearine as the main lipid source to a control diet on shrimp growth and survival as well as on pellet water stability of diets. Test diets were processed containing three levels (0.7%, 1.3% and 2.7%) of either stearine or refined fish oil in a semi-purified control diet. These diets were each assigned to five or six replicated tanks and each tank was stocked with seventeen juvenile shrimp (ca. 0.50 g) in an indoor seawater recirculating system. At the end of 6 weeks, the survival of shrimp was 89.4–95.3% with no significant difference (P > 0.05) among dietary treatments. The six test diets obtained significantly higher (P < 0.05) shrimp growth rates (1.46–1.83 g week−1) than the control diet (1.38 g week−1). The shrimp that were fed the three stearine-added diets exhibited high growth rates (1.75–1.83 g week−1). Increasing the inclusion level of the stearine improved pellet water stability (91.7–93.9%; P < 0.05). These results suggest that stearine can replace fish oil in shrimp feed based on the growth performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".