Characterization of Bioflocs in a No Water Exchange Super-intensive System for the Production of Food Size Pacific White Shrimp <i>Litopenaeus vannamei</i>
Bibliographic record
Abstract
Zero exchange super-intensive recirculating aquaculture systems (RAS) represent an environmentally “friendly” alternative to traditional shrimp culture methods, however the susceptibility to pathogens typically increases as the density of cultured organisms increases. The study describes a greenhouse-enclosed super-intensive RAS, utilizing culture water from a 62-day nursery trial, to grow juvenile (0.99 g) Litopenaeus vannamei , Pacific White Shrimp, to market-size under high stocking density (450/m 3 ). The study evaluated the effects of foam fractionation and settling tank particulate control methods on water quality and microbial particulate related flora in four 40 m3 tanks with two replicates per control method under no water exchange. Microbial communities were distinguished at the gram-stain level using flow cytometric fluorescent activated cell sorter (FACS) methods. Differentiation between all other populations of organisms and particles between 1~20 m m was based upon autofluorescence and forward scatter (a size indicative light parameter) using FACS. Analysis of variance indicated that the microbial communities associated with each particulate control method did not deviate from one another significantly (p>0.05). Gram-positive bacteria were the dominate fraction regardless of particulate control method (p<0.05). Six unique autofluorescence signals were consistently present within each RAS. This study is a first step in using flow cytometry as a tool to document changes in microbial communities in no water exchange super-intensive system for production of marketable shrimp. Shrimp yield and survival was high: 9.34~9.75 kg/m 3 and 94.5%~96.9%, respectively. Further, the data suggest that the use of pre-conditioned water may help to prevent ammonia and nitrite overload and decrease pathogenic organism prevalence.
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) | 0.000 | 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".