Effect on white gut and white feces disease in semi intensive <i>Litopenaeus vannamei</i> shrimp culture system in south Indian state of Tamilnadu
Bibliographic record
Abstract
Litopenaeus vannamei is a new species to India; right now culture technology is not comparable with black tiger shrimp. Bearing all those in mind the present study was carefully carried out. In the present study an attempt has been made to culture the white leg shrimp, L. vannamei in two ponds each with 0.6 ha in Kodakaramulai, Sirkali taluk, Nagai District, Tamilnadu. The salinity of the two ponds was ranging between 22-30 ppt and DO values fluctuated between 4.0 mg/l and 5.0 mg/l in the morning and between 4.5 mg/l and 6.5 mg/l in the evening. Ammonia was recorded maximum 0.3ppm and minimum was 0.1ppm. During the culture after 50 th DOC there was a poor growth observed in both ponds due to white gut and white fecal matter. Immediately feed probiotic ( Bacillus sp) mix with the feed for three weeks and two meals per day. The problem was slowly rectified. The maximum Survival 85% in pond 2 and 82% survival was recorded in pond1. The present study confirm that, shrimp farming community need more awareness to use feed probiotic, proper water qualitymanagement and feed management is essential for the successful culture.
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) | 0.002 | 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".