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Record W4284683306 · doi:10.3390/ijerph19138199

Detection and Concentration of Neonicotinoids and Other Pesticides in Honey from Honey Bee Colonies Located in Regions That Differ in Agricultural Practices: Implications for Human and Bee Health

2022· article· en· W4284683306 on OpenAlexaff
Gilda Ponce-Vejar, Silvia Lizette Ramos de Robles, José Octavio Macías-Macías, Tatiana Petukhova, Ernesto Guzmán‐Novoa

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsUniversity of Guelph
FundersConsejo Nacional de Ciencia y TecnologíaUniversidad de Guadalajara
KeywordsPesticideImidaclopridNeonicotinoidToxicologyHuman healthAgricultureContaminationHoney beeFungicidePesticide residueBiologyEnvironmental healthAgronomyEcologyMedicine

Abstract

fetched live from OpenAlex

This is a preliminary study conducted to analyze the presence and concentration of pesticides in honey obtained from honey bee colonies located in two regions with managed ecosystems that differ in the intensity and technification of agricultural practices. Fourteen pesticides at variable concentrations were detected in 63% of the samples analyzed. The pesticides most frequently found at higher concentrations were insecticides (neonicotinoids, followed by organophosphates), herbicides, and fungicides. The number, frequency, and concentration of pesticides were higher in samples collected from hives located where intensive and highly-technified agriculture is practiced. Forty-three percent of the samples from that zone had residues of imidacloprid, compared with only 13% of the samples from the less-technified zone. Furthermore, 87.5% of those samples had imidacloprid concentrations that were above sublethal doses for honey bees (>0.25 ng/g) but that are not considered hazardous to human health by the European Commission. The results of this study suggest that honey can be used as a bioindicator of environmental contamination by pesticides, which highlights the need to continue monitoring contaminants in this product to determine the risks of pesticide impacts on pollinator health, on ecosystems, and on their potential implications to human health and other non-target organisms.

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)0.002
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 score0.152
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.134
GPT teacher head0.382
Teacher spread0.248 · 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

Citations31
Published2022
Admission routes1
Has abstractyes

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