Production of red tilapia (Oreochromis spp.) in floating cages in the Mekong Delta, Vietnam: mortality and health management
Bibliographic record
Abstract
The Mekong Delta in Vietnam is one of the most productive aquaculture regions in the world, in which the red tilapia (Oreochromis spp.) industry is a small-scale industry that mainly supplies local markets in the delta region. Little is known about the frequency of mortality events and health management in this sector. We describe red tilapia floating cage production systems in the Mekong Delta, Vietnam, for the purposes of quantifying mortality and associated production factors, and describing practices that may influence pathogen introduction and spread to and from farms. In July 2014, approximately 50 red tilapia farmers from 4 provinces (201 farmers in total) were randomly selected and interviewed. Median overall perceived mortality (PM) within a production cycle was 35%. Overall PM was found to be affected by province (p < 0.01), age of farmers (p = 0.01), anticipated main reason for PM in the first 2 wk (p = 0.03), most common market for the fish (p = 0.02), and whether farmers recorded stocking information (p = 0.01). Based on the interviews, we describe and discuss processes that potentially affect pathogen introduction and spread on these farms, such as movements of live and dead fish, distances between farms, mechanical transmission, and biosecurity practices such as treating fish before stocking, using disinfectants, and sharing equipment, and harvesters' movements. This study provides fundamental understanding of red tilapia aquaculture management in the Mekong Delta, and describes management factors that could become important in the event of disease outbreaks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".