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Record W2020597219 · doi:10.1002/etc.277

Comparison of the effects of glyphosate and atrazine herbicides on nontarget plants grown singly and in microcosms

2009· article· en· W2020597219 on OpenAlexafffund
Rebecca L. Dalton, Céline Boutin

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrocosmGlyphosateAtrazineBiologyEnvironmental chemistryPesticideChemistryEcology

Abstract

fetched live from OpenAlex

Intensification of agriculture and the corresponding increase in herbicide use has led to concern regarding the effects these chemicals may have on nontarget plants of agroecosystems. Current pesticide registration guidelines are focused on testing crop species grown singly in pots under greenhouse conditions and may not provide adequate measures of protection to noncrop species. The objective of the present study was to compare the response of terrestrial and wetland plants to the herbicides glyphosate and atrazine when grown singly in pots versus under different microcosm conditions. Greenhouse microcosms were generally more sensitive than single-species tests. Plants grown for an extended test period or in seminatural field conditions were generally less sensitive to herbicides. Sensitivity was found to be dependent on interactions between species and test conditions. Changes in community structure were observed in herbicide-treated microcosms that would not be predicted from single-species testing. Single-species tests are useful because they are inexpensive, can demonstrate clear dose-response patterns uncomplicated by other factors influencing growth, and are able to provide a measure of the sensitivity of a given species to glyphosate and atrazine. However, they are unable to predict subtle changes in community structure that may have important long-term consequences.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.122

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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 designBench or experimental
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

Citations44
Published2009
Admission routes2
Has abstractyes

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