Keynote address I: Sensing issues in the automated monitoring of the quality of drinking water
Bibliographic record
Abstract
This talk will address several important aspects of sensing and representation of water quality in a spatiotemporal manner for automation of the entire process. The addressed key issues are: (a) Automated and remote sensing issues of water quality monitoring; (b) Signal processing and sensory data process; (c) Development of an effective water quality index; (d) Network architecture of the water quality monitoring system. Water comes into contact with minerals, salt, vegetation, toxic chemicals, and biological waste, and is never completely pure. Many of these contaminants may pose health risks. About 3.4 million people die every year in the world due to waterborne diseases, and poor sanitation. Regular monitoring of the quality of drinking water and taking proper actions to improve its quality is important for healthy living. This is particularly important in rural areas and underprivileged communities. Our proposed system consists of multiple sensor nodes that are geographically distributed and have the capability of wireless communication to local microcontrollers. After some basic processing, the gathered information is transmitted by the microcontrollers to a central assessment unit. The system analyzes the geographic and temporal information and provides advisories, warnings, trends, forecasts, and suggested actions. Robustness, speed, low-cost, and user-friendliness are key features of the developed system. The talk will present theoretical, research, and practical aspects of the proposed developments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".