Energy Modeling of an Aquaculture Raceway
Bibliographic record
Abstract
Abstract Large seasonal temperature variations in aquaculture source water leave aquaculture ponds and raceways susceptible to temperature variations leading to nonoptimal growing conditions. Such conditions may slow down the growth rate and make aquatic species vulnerable to disease and potential death, leading to economic setback for aquaculture farmers. Therefore, it is advantageous to predict the temperature of aquaculture raceways under the influence of seasonal variations and study the parameters that contribute to these variations. This allows one to develop strategies and processes to better regulate the raceway temperature to maximize its productivity. A numerical energy model was developed to simulate the temperature of water inside an aquaculture raceway, and a parametric study was conducted to investigate the influence of various key parameters on the raceway temperature. It was found that surface area and flowrate have a large effect on the raceway temperature, while depth of raceway had little effect. The largest surface area tested produced outlet temperatures and heat transfer values that were 6.2% and 76% higher, respectively, than the smallest surface area tested. Decreasing flowrate from the reference value of 43 L/s to 1 L/s resulted in an 83% increase in average outlet temperature. It was also observed that the variations in the ambient air temperature alone have negligible effect on the raceway temperature. The model was further implemented to simulate the temperature of raceways located at different geographical locations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".