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Record W7117730066 · doi:10.1088/2515-7620/ae3227

Exceptional use: examining methyl bromide applications in California 2016–2022

2025· article· en· W7117730066 on OpenAlexaboutno aff
Yoshira Ornelas Van Horne, Jill Johnston

Bibliographic record

VenueEnvironmental Research Communications · 2025
Typearticle
Languageen
FieldMedicine
TopicPoisoning and overdose treatments
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsFumigationMontreal ProtocolQuarantineBromidePesticideChemical safetyHazardous waste

Abstract

fetched live from OpenAlex

Methyl bromide (MeBr) has been widely used as a fumigant to control for pests, fungi and weeds as well as for disinfection of warehouses, shipping containers, and other commodities. MeBr is a known developmental, neurologic and respiratory toxin. Due to its ozone-depleting properties, MeBr was listed under the Montreal Protocol in 1992. While MeBr use was set to phase out by 2005, the Montreal Protocol and the US Clean Air Act allows critical use exemptions, such as fumigation of freight containers for quarantine and preshipment purposes. To evaluate state-wide spatial and temporal patterns, we examine publicly available pesticide data on the use of MeBr in California from 2016-2022. We found that MeBr applications continue in 36 out of 58 CA counties. For non-agricultural fumigation applications (e.g., commodity fumigation) of MeBr from 2016-2022, a total of 582,050 kilograms (1,283,201 pounds) were applied across 25 counties in CA, home to 24 million people. Los Angeles ranks as the highest use county, with a total of 269,571 or 46% of the total kilograms (594,302 pounds) applied from 2016-2022 for non-agricultural fumigation applications. Additionally, we characterized ambient MeBr concentrations in the West Long Beach community of LA County, based on a state monitor active since 2023, observing concentrations exceeding CA standards. This study underscores the importance of evaluating chemical phaseouts and improving enforcement and monitoring to ensure public health protections.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.126
GPT teacher head0.427
Teacher spread0.301 · 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 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
Published2025
Admission routes1
Has abstractyes

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