Development of Smart Waste Bin for Solid Waste Management
Bibliographic record
Abstract
Growing urbanisation in developing countries, population growth, and changes in human activities and consumption patterns have resulted in significant amounts of trash that must be appropriately disposed of, treated, and managed to provide a sustainable environment and a reasonable standard of life for the growing population. The aim of the paper is to design a smart dustbin for proper disposal of waste without any human intervention by providing a smart technology for waste system monitoring, reducing human time, effort, and intervention. This paper presents a smart waste bin integrated with a microcontroller-based Arduino board which is interfaced with ultrasonic sensors, MQ-2 sensor, servo motor, LCD and GSM modem. The Arduino microcontroller is programmed using Arduino C which measures the height of the dust bin using the ultrasonic sensor. Once the waste gets to the pre-set level, the microcontroller activates the GSM modem to send a message to a designated number. The status of the waste in the bin is transferred to the designated line and display on the LCD whenever it exceeds the pre-set value. The replacement of the traditional waste bin with smart waste bin help in efficient management of waste by assuring that filled waste bin are emptied when the pre-set value is exceeded. This also help in reducing time involve in checking the status of the waste bin and number of trips embarked by the waste collection vehicle and total expenditure associated with collection is minimized. It eventually helps to maintain cleanliness in our environment. Therefore, the system makes the waste collection more efficient.
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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 | 0.000 |
| 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".