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Record W4293010396 · doi:10.11159/icsta22.151

Development of a Multi Pollutant Model to Assess Air Pollution Association with Human Health Effects

2022· article· en· W4293010396 on OpenAlexafffund
Shannon Jarvis, Wesley S. Burr

Bibliographic record

VenueProceedings of the International Conference on Statistics, Theory and Applications (ICSTA ...) · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsTrent University
FundersHealth Canada
KeywordsPollutantHuman healthAir pollutionAssociation (psychology)Environmental sciencePollutionComputer scienceEnvironmental healthEnvironmental planningEcologyBiologyMedicinePsychology

Abstract

fetched live from OpenAlex

A number of methodologies have been developed for investigation of the association between human health and exposure to a single pollutant [e.g., 1].However, as pollutants are correlated, and the joint effect of pollutants is of high interest, work continues on development for multiple pollutant models.In this work, we discuss a method using Thin Plate Splines (TPS) to simultaneously model both PM2.5 (particulate matter less than 2.5 μm in aerodynamic diameter) and O3 (ozone) in association with human mortality.The results are compared to effect estimates obtained from single pollutant models.We find similar temporal trends in the estimates, with large movements in both PM2.5 and O3 being captured in the TPS estimates.The estimated errors for the TPS method are larger than the individual models combined and produce risks that are comparable but slightly elevated.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.073
GPT teacher head0.349
Teacher spread0.276 · 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 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
Published2022
Admission routes2
Has abstractyes

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