MétaCan
Menu
← Back to cohort
Record W4233050680 · doi:10.32920/ryerson.14652681.v1

Environmental stress effects on illness in Southern Ontario

2021· preprint· en· W4233050680 on OpenAlexaboutno aff
Chelsea Blair LeBlanc

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsWindsorAir pollutionGeographyEnvironmental scienceEnvironmental healthMedicineEcology

Abstract

fetched live from OpenAlex

A spatial analysis of smog events in Southern Ontario and prevailing winds reveals various patterns that occur during smog advisories. Smog events cause numerous excess deaths and illnesses each year throughout Southern Ontario due to high levels of air pollutants that are generated in North America. Cardiovascular and respiratory illnesses are the main hospital admissions that occur during summer smog episodes. These effects are experienced throughout regions located along the Windsor-Quebec corridor, but there are variations in the numbers of affected people due to the effects of surrounding geographical features and the local contribution of air contaminants. Meteorological differences play a major role in the effects of smog events with factors such as temperature and prevailing winds. This study examines the effects of long distance transport of contaminants from origins in the United States into Canada as indicated by respiratory and cardiovascular mortality and morbidity effects during 9 smog events. This study found that during certain conditions there is a correlation between wind direction and smog related mortality and morbidity.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.018
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.021
GPT teacher head0.261
Teacher spread0.240 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicAir Quality and Health Impacts→French-language works237,207→