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Record W4389381307 · doi:10.1289/ehp12768

The Effect of Pesticide Spray Season and Residential Proximity to Agriculture on Glyphosate Exposure among Pregnant People in Southern Idaho, 2021

2023· article· en· W4389381307 on OpenAlexaff
Cynthia L. Curl, Carly Hyland, Meredith Spivak, Lianne Sheppard, Bruce P. Lanphear, Michael Antoniou, María Ospina, Antonia M. Calafat

Bibliographic record

VenueEnvironmental Health Perspectives · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsSimon Fraser University
FundersNational Institute of Environmental Health SciencesCenters for Disease Control and PreventionU.S. Department of Health and Human Services
KeywordsGlyphosatePesticideAgricultureEnvironmental healthGeographyEnvironmental scienceEnvironmental protectionMedicineBiologyAgronomyArchaeology

Abstract

fetched live from OpenAlex

BACKGROUND: Glyphosate is one of the most heavily used pesticides in the world, but little is known about sources of glyphosate exposure in pregnant people living in agricultural regions. OBJECTIVE: Our objective was to evaluate glyphosate exposure during pregnancy in relation to residential proximity to agriculture as well as agricultural spray season. METHODS: and 18, respectively). RESULTS: ). DISCUSSION: Pregnant people living near agriculture fields had significantly increased urinary glyphosate concentrations during the agricultural spray season than during the nonspray season. They also had significantly higher urinary glyphosate concentrations during the spray season than those who lived far from agricultural fields at any time of year, but concentrations did not differ during the nonspray season. These findings suggest that agricultural glyphosate spray is a source of exposure for people living near fields. https://doi.org/10.1289/EHP12768.

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)0.001
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 score0.059
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.005
GPT teacher head0.233
Teacher spread0.228 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Health PerspectivesSame topicPesticide and Herbicide Environmental StudiesFrench-language works237,207