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Record W2942861969 · doi:10.13034/jsst.v11i1.307

A Comparison of Particulate Matter Exposures Between a Student’s Private Vehicle and Public Bus Transit Commutes

2019· article· en· W2942861969 on OpenAlexvenueno aff
Tirth M Patel

Bibliographic record

VenueJournal of Student Science and Technology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsPublic transportEnvironmental scienceParticulatesTransport engineeringAir pollutionEnvironmental engineeringAutomotive engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

Daily commuters of public transportation and private vehicles are exposed to a wide range of traffic-related air pollution (TRAP). However, evidence of differences between commuting method has been building. In this study, the personal ultrafine particle (UFP) and black carbon (BC) air pollution exposures of a high school student were measured during their daily commute. In total, 39 commutes made between the student’s home and school were measured. These commutes were either by bus or private vehicle. Data was analysed using box plots and T-tests of statistical significance. Levels of BC were not significantly higher on buses (mean(SD) = 849(645) ng/m3) than cars (650(689) ng/m3) (p-value = 0.199). For UFP, levels were significantly higher for bus commutes (9393(6923) pts/cm3) than those of private vehicle (4234(6446) pts/cm3) (p-value = 0.045). Our findings suggest that bus commuters may experience higher exposure to UFP relative to private vehicle commuters. The higher UFP exposure may be accounted by the fact that city buses can have a higher air exchange rate due to the constant opening of doors. As well, buses are mainly diesel vehicles, which are a strong source of UFP.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.046
GPT teacher head0.362
Teacher spread0.316 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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