Optimizing Shrimp Culture Through Environmental Monitoring: Effects of Water Quality and Metal Ion Profile on Whiteleg Shrimp (Litopenaeus vannamei) Performance in a Semi-Intensive Culture Pond
Bibliographic record
Abstract
Water quality management is crucial for sustainable whiteleg shrimp (Litopenaeus vannamei) aquaculture, though little research has comprehensively investigated the spatiotemporal fluctuation of trace elements in tropical semi-intensive ponds. This study investigated the water quality variations and trace element concentrations in an earthen pond across a 56-day culture cycle during the dry season. Physicochemical parameters (temperature, pH, salinity, dissolved oxygen, ammonia, nitrite, and nitrate) and trace elements (Cu, Zn, Mn, Fe, and Mg) were measured concurrently with shrimp growth and survival. The DO and pH readings were observed to fluctuate significantly during the mid-to-late stages of culture, with DO nearing critical thresholds (<5.0 mg L−1). A sudden increase in ammonia and nitrite levels suggested the accumulation of organic matter and a microbial imbalance. Zinc concentrations (0.28–1.00 mg L−1) approached stress-inducing levels, while magnesium remained low (10.44–10.72 mg L−1). Pearson’s correlation revealed strong positive associations between ammonia and nitrate (r = 0.95) and between DO and pH (r = 0.94), while Mg was negatively correlated with Fe (r = −0.99) and nitrite (r = −0.88). Shrimp achieved 13.43 ± 0.73 g mean weight, with 77.8% survival and an FCR of 1.08. These results provide baseline evidence that combined water quality and trace element monitoring can become an early warning framework for pond management. Future studies integrating shrimp physiology and immune responses are needed to establish direct causal relationships.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".