Characteristics of physical water quality variation under heat storage in closed saline water aquaculture ponds in the tropics
Bibliographic record
Abstract
Abstract. In tropical closed saline water aquaculture ponds, intermittent rainfall during the rainy season and strong solar radiation can cause salinity and thermal stratification, respectively. The combination of these stratifications may result in specific convection, known as thermohaline convection. Thermohaline convection is a type of double-diffusive convection that is observed in water bodies with two water densities. Thermohaline convection occurring in closed saline water ponds can induce heat storage in them. Heat storage in saline water aquaculture ponds is thought to be an important factor for controlling pond water quality, as the water environment can be affected by high water temperatures. Moreover, because heat storage is induced under the specific conditions of salinity stratification, salinity stratification also impacts physical water quality parameters. To clarify the relationship between heat storage and the variations in physical water quality associated with the formation of salinity stratification in saline water aquaculture ponds, continuous monitoring of weather and physical water quality parameters was conducted under culture conditions of fish and shrimp in Thailand. The results indicate the following: (1) Heat storage triggered by the rainfall-induced formation of salinity stratification can occur in shrimp culture ponds but is less likely to occur in fish culture pond; this is because the swimming behavior of fish can make formation of density stratification difficult. (2) When heat storage occurred in saline water aquaculture ponds, water temperature, turbidity, and dissolved oxygen were highly correlated in the heat storage layer, suggesting that high water temperature might affect microorganism activity in the ponds.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".