Water Quality Monitoring and Control System for Fish Farmers Based on Internet of Things
Bibliographic record
Abstract
Numerous individuals in Riau Province are engaged in the fish cultivation due to the province's ample water supply and the substantial market demand, which generates relatively high selling prices.The significance of water quality issues in fish aquaculture stems from the fact that fish inhabit water.Water quality is determined by a number of parameters, including acidity, temperature, turbidity, and the quantity of dissolved substances.Therefore, this study aimed to develop a device capable of monitoring water quality.The water parameters observed through the utilization of sensors and the Internet of Things were temperature, acidity, dissolved solids concentration, and turbidity.Utilizing the Rapid Application Development (RAD) Method, the research was completed through the following phases: Analysis, design, development, evaluation, implementation, and simulation.A device comprised of electronic components, including a microcontroller and sensors, was the result of this research.Monitoring parameter data was collected in real time via sensors and subsequently stored on a cloud server.The tools developed as a consequence of this research enable fish producers to acquire parameter data in real time.These tools serve the purpose of generating responses to normalize water quality and to facilitate action in response to changes in water quality.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".