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Record W4413683005 · doi:10.1021/acs.jafc.5c03867

Herbicides in Use: Current Status and Perspectives in the Different Biogeographic Regions of Europe

2025· review· en· W4413683005 on OpenAlexaff
Agnieszka Synowiec, Marta Czekaj, Mercedes Verdeguer, Claudia Campillo-Cora, Yedra Vieites‐Álvarez, David López-González, Adela M. Sánchez‐Moreiras, David Fernández‐Calviño, F.F. Nocito, Carla Ragonezi, Miguel Â. A. Pinheiro de Carvalho, Merit Sutri, Merrit Shanskiy, Sigrún Dögg Eddudóttir, Tetiana Fedoniuk, Andrea Vityi, Ursula Bürgener, Mihai Gidea, Francisco Espinosa Escrig, Gülçin Beker Akbulut, Alicia Morugán‐Coronado, Esther Valiño, Fabrizio Araniti

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2025
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsChemistry Industry Association of Canada
FundersHORIZON EUROPE Food, Bioeconomy, Natural Resources, Agriculture and Environment
KeywordsCurrent (fluid)GeographyEcologyBiologyOceanographyGeology

Abstract

fetched live from OpenAlex

This review examines the use of herbicides across Europe's biogeographical regions, focusing on their historical development, regulatory framework, and environmental impacts. Since the 20th century, the use of herbicides has significantly increased agricultural productivity. However, the continuous use of herbicides with the same mode of action can lead to the development of resistant weeds, especially when low-diversity weed management strategies are employed. The European Union has established a strict approval process for herbicidal substances to safeguard environmental and human health. Consequently, the number of authorized active ingredients has declined due to concerns over their adverse effects. This review highlights the need for new sustainable tools for weed control and advocates reassessing Europe's dependence on chemical herbicides, encouraging integrated weed management approaches and policies that balance productivity with environmental protection for a sustainable agricultural future.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.045
GPT teacher head0.262
Teacher spread0.217 · 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 designOther design
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

Citations5
Published2025
Admission routes1
Has abstractyes

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