Deep-learning based approach for forecast of water quality in intensive shrimp culture ponds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the enormous development of aquaculture, reducing the impacts of effluent discharge and improving water quality had become a critical global environmental concern. It is important to assess and predict water quality in the environmental management process of shrimp mariculture. Meanwhile, the accurate forecast of water quality is still in the exploration stage at present. In this study, deep belief networks (DBN) model are used to forecast water quality in intensive shrimp culture. This method based on deep learning includes a five-layered structure to extract relationships between the quantitative characteristic of water bodies and water quality variables. The water quality can be forecasted by the Canadian Water Quality Index (WQI) obtained from the output layer of simulated model. The results show that the DBN model has a great potential to predict the water quality and the ability of generalization and accuracy of model are satisfied.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it