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Record W2883975910 · doi:10.3390/ijerph15081577

Aeroallergens in Canada: Distribution, Public Health Impacts, and Opportunities for Prevention

2018· review· en· W2883975910 on OpenAlexafffundabout
Cecilia Sierra-Heredia, Michelle L. North, Jeff Brook, Christina Daly, Anne K. Ellis, Dave Henderson, Sarah B. Henderson, Éric Lavigne, Tim K. Takaro

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2018
Typereview
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsUniversity of OttawaEnvironment and Climate Change CanadaQueen's UniversityBC Centre for Disease ControlUniversity of British ColumbiaPublic Health OntarioUniversity of TorontoHealth CanadaKingston General HospitalSimon Fraser University
FundersSimon Fraser University
KeywordsPublic healthDistribution (mathematics)Environmental healthEnvironmental planningAir pollutionDiversity (politics)AsthmaClimate changeEnvironmental resource managementGeographyMedicineEcologyEnvironmental sciencePolitical scienceBiologyImmunology

Abstract

fetched live from OpenAlex

Aeroallergens occur naturally in the environment and are widely dispersed across Canada, yet their public health implications are not well-understood. This review intends to provide a scientific and public health-oriented perspective on aeroallergens in Canada: their distribution, health impacts, and new developments including the effects of climate change and the potential role of aeroallergens in the development of allergies and asthma. The review also describes anthropogenic effects on plant distribution and diversity, and how aeroallergens interact with other environmental elements, such as air pollution and weather events. Increased understanding of the relationships between aeroallergens and health will enhance our ability to provide accurate information, improve preventive measures and provide timely treatments for affected populations.

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 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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.282
GPT teacher head0.436
Teacher spread0.154 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations31
Published2018
Admission routes3
Has abstractyes

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