A SPATIAL-AND-SCALE-DEPENDANT MODEL FOR PREDICTING MSW GENERATION, DIVERSION AND COLLECTION COST BASED ON DWELLING-TYPE DISTRIBUTION
Bibliographic record
Abstract
Comprehensive models were developed to predict waste generation for different collection streams. Taking into account the dwelling-type distribution encountered during the different waste collections, it was possible to better capture the waste generation variability. Using the same approach, collection and transportation cost models were also developed. This series of models were validated using data from the Urban Agglomeration of Montreal (UAM), which is composed of 33 districts with widely different scales of population and dwelling characteristics. The unknown parameters of the models were identified through mean square regressions applied on the real data available for the case-study. For example, values of 1.364, 1.019 and 0.500 t/(dwelling.yr) were identified for the total quantity of wastes generated in single-family, duplex and other dwelling, respectively. Using the same approach, it was possible to determine collection time as a function of the dwelling-type distribution along the collection route. Values of 28.7 s, 11.4 s and 5.22 s were identified as the collection time per dwelling for single-family, duplex and other dwelling, respectively. Equipped with a combination of fitted parameters and reported values from the literature, the models were used as predictive tools. Three features are illustrated in this paper: 1) the simulation of various scales for the generation, diversion and specific collection cost; 2) the effect of adding a new collection stream; 3) the effect of an increase of the citizen participation to a specific collection stream. Predicted results enable decision-makers to have access to very useful information.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".