Fuel consumption estimation for kerbside municipal solid waste (MSW) collection activities
Bibliographic record
Abstract
Fuel consumption during seven different daily activities of a garbage co-collection truck and a normal packer truck was estimated from the trucks' global positioning system (GPS) data and fuel consumption records. The co-collection and the normal garbage packer consumed approximately 1.8 L and 1.26 L of diesel per km, respectively, while travelling within the collection areas. Using these fuel rates and the GPS data, the results show that both types of trucks consumed more than 60% of daily total fuel while actually collecting waste on the route. The average daily fuel consumption was 2-4 times higher on rural routes compared to urban areas. Fuel consumption varied significantly depending on the housing density along the collection route. In addition, approximately 5-6 times as much fuel was required to collect a kilogram of waste on a rural route compared to an urban route. Potential methods of reducing fuel consumption were examined. Consistent use of optimal collection routes could potentially save an average of 7.5 L of fuel per truck per day. Reducing the loading time per stop was also studied, but the results suggest that this method does not have significant potential to reduce fuel consumption.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".