Solidarity Behaviour for Optimizing the Waste Selective Collection
Bibliographic record
Abstract
The problem of managing selective collection of waste using containers inside historic city centres can be performed in three sequential phases: first, locating containers along the streets; then, determining the minimum fleet size required to perform all collecting services; and finally, identifying the optimal collection routes.Obviously, the result of the first phase highly influences the procedure since this will determine the decisions to be taken for the subsequent phases.This paper is focused on the first phase.Facility-customer distances, the size of container groups and the cost of installing those containers in specific sites along the streets are parameters to be considered when designing the collecting system.Additionally, we assume that customers are willing to have a solidarity behaviour, which consists of using a container assigned to them within a pre-established proximity radius, although the assigned container may not be necessarily the closest to their residence.For this scenario, a more efficient deployment of containers for selective collection of urban solid waste can be obtained.To illustrate the performance of the developed methodology, a computational experience has been carried out on a network with randomized data based on a zone belonging to city of Seville (Spain).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".