Effects of Algae and Shell Pigment Extract‐supplemented Diets on Shell Pigmentation and Growth Performance of Pacific Abalone, <i>Haliotis discus hannai</i>
Bibliographic record
Abstract
Abstract To improve shell pigmentation and growth performance of Pacific abalone, five extruded diets were prepared by supplementing a formulated control diet with 3% each of three species of algae (Pacific dulse, Porphyra yezoensis, Spirulina) and a pigment extract from the abalone shells (0.02%). Each of the five diets and a commercial feed were randomly assigned to three containers each stocked with 1000 juvenile Pacific abalone (2 g per individual) in a flow‐through seawater system for a 5‐mo feeding trial. Test results showed: (1) all the supplements significantly (P < 0.05) increased percentages of dark‐brown shelled abalone in the test treatments, relative to the control treatment; (2) the abalone fed the experimental diets achieved significantly (P < 0.05) greater final weight, shell‐length growth rates, and higher meat protein contents than those fed the commercial feed (P < 0.05); and (3) the abalone fed the Spirulina‐supplemented diet achieved the best overall growth, pigmentation performances, and feed conversion ratio; and the Pacific dulse supplement generated the highest protein content in abalone meat product (P < 0.05) among the tested diets. These results demonstrate the potential of locally made feed, which can generate desirable characteristics in abalone under aquaculture conditions.
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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) | 0.000 | 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".