Design And Development Of A Quality And Quantity Water Monitoring System For Water Tank Based On Internet Of Things
Bibliographic record
Abstract
In the context of urban life, the quality and quantity of water stored in tanks play an indispensable role. However, concerns over the lack of effective monitoring of water conditions in tanks have garnered significant attention. This research aims to design a monitoring system utilizing Internet of Things (IoT) technology to ensure continuous monitoring of water quality and quantity within tanks. The study zeroes in on developing a solution that overcomes the limitations of traditional methods, known for their time and cost constraints. This solution hinges on a real-time monitoring system that integrates sensors and the NodeMCU ESP32 microcontroller. Measurements of water quality parameters, such as turbidity and total dissolved solids (TDS), along with assessments of water quantity through tank volume, are integrated into the system. Software development is carried out using the Arduino IDE platform. Through this research, an increased awareness of the importance of monitoring water quality in tanks is anticipated, and this solution is expected to make a significant contribution to the development of efficient and effective monitoring technology. Thus, a crucial step toward more accurate and up-to-date water monitoring in urban environments can be achieved.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".