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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 OpenAlex
Shannon Jarvis, Wesley S. Burr

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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 m 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.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.505
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.073
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