Application of dried shrimp head in the diets of sea cucumber Holothuria scabra
Bibliographic record
Abstract
This study aims to determine the suitability of dried shrimp head as part of the ingredient in formulated diet to promote growth of sea cucumber in captivity. The sea cucumber Holothuria scabra were divided into two tanks to test two different diets: Diet A (commercial diet supplemented with dried shrimp waste) and Diet B (commercial diet without dried shrimp waste). A total of 20 pieces of H. scabra per tank for each diet were involved in the experiment that lasted for 8 weeks. The weight increment of H. scabra from each tank was recorded weekly and survival rate (SR), weight gain rate (WGR) and specific growth rate (SGR) was calculated. Overall, H. scabra fed with Diet A showed higher WGR than H. scabra fed with Diet B, with increment of 30.57 % (12.10 g) recorded in Diet A to 14.26 % (11.18 g) for Diet B from their average initial weight. The overall SGR of Diet A and Diet B were 0.48 % and 0.24 %, respectively. SR remaining at 100 % for all treatments. Statistical analysis showed the average wet weight of Diet A was significantly higher (p<0.05) than Diet B. Results showed the ability of sea cucumber H. scabra to consume and digest diet containing dried shrimp head, suggested that there is potential in using dried shrimp head to promote growth rates of sea cucumber. Using dried shrimp heads can be considered to make good use of shrimp farming waste.
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) | 0.000 | 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) | 0.001 | 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".