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Record W4386214186 · doi:10.3386/w31593

Seasonal Allergies and Accidents

2023· report· en· W4386214186 on OpenAlexaff
Mika Akesaka, Hitoshi Shigeoka

Bibliographic record

VenueNational Bureau of Economic Research · 2023
Typereport
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsSimon Fraser University
FundersJapan Society for the Promotion of ScienceMinistry of Internal Affairs and CommunicationsTokyo Center for Economic ResearchInamori Foundation
KeywordsAllergyMedicineImmunology

Abstract

fetched live from OpenAlex

Although at least 400 million people suffer from seasonal allergies worldwide, the adverse effects of pollen on "non-health" outcomes, such as cognition and productivity, are relatively understudied.Using ambulance archives from Japan, we demonstrate that high pollen days are associated with increased accidents and injuries-one of the most extreme consequences of cognitive impairment.We find some evidence of avoidance behavior in buying allergy products but limited evidence in curtailing outdoor activity, implying that the cognitive risk of pollen exposure is discounted.Our results suggest that policymakers may wish to consider programs to raise public awareness of the risk and promote behavioral change.

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)0.002
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: Other · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.470
GPT teacher head0.547
Teacher spread0.077 · 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
GenreOther

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

Citations2
Published2023
Admission routes1
Has abstractyes

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