Optimal Feed Formulation for Abalone Growth: Kelp-Ulva-Spirulina Synergy via RSM
Bibliographic record
Abstract
This study investigates the natural feed supplements (kelp, ulva, and spirulina) on abalone growth, offering a nutritional solution to problems faced in abalone farming, like insufficient nutrient availability and reliance on expensive commercial feeds.It is a 3month experiment with a sample size of 50 abalones per treatment group.Using Response Surface Methodology, quadratic models predicting abalone growth based on supplement percentages were developed.The study found that kelp and spirulina feed supplements had the most significant and least significant impact on abalone growth, respectively.The optimal feed composition was discovered at 8.63557% kelp, 8.83486% ulva, 6.06173% spirulina, yielding a 25.17% increase in weight and a 17.42% increase in shell width compared to the control group.This has yielded 3.34857 g/month and 1.07576 mm/month as the predicted responses for abalone weight gain and shell width.The high model's determination coefficient (R² = 0.965) and statistically significant regression coefficients (p = 0.0190) established its reliability and accuracy.The research's outcomes integrate numerical applications to make them practical for Abalone breeders, leading to precise predictions and adjustments of feed composition at the optimal level to enhance abalone growth and help in refining abalone farming management techniques to achieve optimized farming practices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".