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Record W3126162220

Exploring Patterns of Environmental Injustice in Ambient Air Pollutant Levels in British Columbia

2020· article· en· W3126162220 on OpenAlexaboutno aff
Sumara Stroshein

Bibliographic record

VenueSFU Undergraduate Research Symposium Journal · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsAir pollutionGeographyInjusticeSocioeconomic statusPollutantDistribution (mathematics)Environmental healthEnvironmental justiceAir pollutantsLogistic regressionSocioeconomicsEnvironmental protectionMedicineEcologySociologyPsychologyPopulation
DOInot available

Abstract

fetched live from OpenAlex

Research in major Canadian cities has demonstrated that regions characterized by socioeconomic deprivation tend to have higher levels of ambient air pollution. However, the distribution of the burden of air pollution across rural BC has not been thoroughly investigated, even in areas with major industrial polluters. This project used data from the Canadian Urban Environmental Health Research Consortium (CANUE) to explore the association between PM2.5 and NO2 levels and measures of deprivation in rural BC. Preliminary analysis using logistic regression models suggests that in the South Interior region of BC, postal code regions with the highest deprivation scores are 2.45 times more likely to have a PM2.5 level above the regional mean (CI: 2.20-2.74) compared to postal codes with the lowest deprivation scores. This preliminary analysis supports the idea that there are inequities in the distribution of ambient air pollutants in both rural and urban regions of the province.

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)0.002
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 score0.023
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.181
GPT teacher head0.340
Teacher spread0.159 · 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
Published2020
Admission routes1
Has abstractyes

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