Codular Waste Monitoring System for Organic and Non-Organic Waste Based on Internet of Things
Bibliographic record
Abstract
The Internet of Things (IoT)-based waste bin monitoring system is an innovative solution in managing and monitoring waste efficiently.This research aims to develop a Codularbased bin monitoring application that can monitor organic and non-organic waste data in real-time.The system uses HC-SR04 ultrasonic sensor to measure the height of the waste pile with an accuracy range of ±2 cm and a response time of less than 1 second.A loadcell sensor is used to detect the weight of the waste with an accuracy of ±0.1 kg.The data collected by the sensors is transmitted to the application through the ESP8266 WiFi module, ensuring the reliability of the system in providing information in a timely manner.The app displays data on the height and weight of waste, both organic and non-organic, and provides notifications when the maximum capacity is reached.With this implementation, waste management can be more effective, encourage community participation in keeping the environment clean, and reduce the negative impact of waste on the environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".