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Record W4413315394 · doi:10.1080/13683500.2025.2545529

Estimating floating population based on the impact of solid waste generation

2025· article· en· W4413315394 on OpenAlexaff
Júlio da Silva Dias, Marcelo Luiz Brocardo, Adriano de Amarante, Rafael Tezza, Issa Traoré

Bibliographic record

VenueCurrent Issues in Tourism · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMunicipal solid wastePopulationEnvironmental scienceWaste managementNatural resource economicsBusinessEconomicsEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.351
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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