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Record W7132953727

A Multi-Pollutant Evaluation of Intraurban Spatial and Socioeconomic Disparity in Exposure in Hamilton, Canada

2024· dissertation· W7132953727 on OpenAlexaboutno aff
Elysia G. Fuller-Thomson

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsAir pollutionPollutionPollutantAir quality indexAir pollutantsSocioeconomic statusSulfur dioxideNitrogen dioxide
DOInot available

Abstract

fetched live from OpenAlex

Environmental inequities have long been documented in air quality exposure; however, previous research focuses on just a few pollutants. A year-long passive air monitoring campaign investigating intraurban pollution variation was undertaken within Hamilton and Burlington, Canada for the following pollutants: nitrogen oxides (NOx, NO2), ozone (O3), sulphur dioxide (SO2), and polycyclic aromatic compounds (PACs). NO2 and O3 varied widely across the city, and SO2 had elevated concentrations only downwind of industry. PACs were elevated yet varied dramatically across the city. Pollution surfaces of NO2, O3 and PAC toxicity were developed by land-use regression models. Pollutant surfaces were evaluated for environmental inequality by assessing their association with the dimensions of the Ontario Marginalization Index using a spatial lag regression model. There was inadequate evidence of exposure inequality for most pollutants. This indicates that patterns of inequalities related to Canadian air pollution are not universal, rather, they are city- and pollutant-specific.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.036
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.359
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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