Estimating floating population based on the impact of solid waste generation
Bibliographic record
Abstract
This paper presents a model for predicting the size of the floating population which correlates the municipal solid waste (MSW) collection data with the city population. The model helps public managers evaluate and improve the impact of seasonal population fluctuation on solid waste generation, enabling better resource planning and utilisation year-round. The model was developed and validated using several years of data collected in Florianópolis (Brazil). Using waste generation as a proxy to estimate the number of inhabitants presents challenges, particularly in defining solid waste and determining the types of waste contained in municipal waste. The level of detail in waste collection data significantly impacts calculation accuracy. It is also possible to link data from the project database to databases from other public service providers, such as public transportation and telecommunications. Practical implications of the methodology described in this paper relate, for example, to the management of seasonal tourism in a large municipality whose public services are impacted by fluctuations in the population. This approach helps the municipality in its effort for increasing the effectiveness of resource allocation while, at the same time, increasing the quality of the services provided to citizens and tourists.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".