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Record W2583533575 · doi:10.1139/cjps-2016-0358

Response of glyphosate-resistant horseweed [Conyza canadensis (L.) Cronq.] to a premix of atrazine, bicyclopyrone, mesotrione, and S-metolachlor

2017· article· en· W2583533575 on OpenAlexvenueno aff
Debalin Sarangi, Amit J. Jhala

Bibliographic record

VenueCanadian Journal of Plant Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsnot available
FundersUniversity of Nebraska-Lincoln
KeywordsMesotrioneAtrazineGlyphosateBioassayAnimal scienceBiologyAgronomyPesticideEcology

Abstract

fetched live from OpenAlex

A premix of atrazine, bicyclopyrone, mesotrione, and S-metolachlor has recently been commercialized for pre-emergence (PRE) and early post-emergence (POST) control of broadleaved and annual grass weeds in corn in the United States. Field and greenhouse dose-response bioassays were conducted in 2015 and 2016 to evaluate the response of glyphosate-resistant (GR) horseweed to this premix applied before or after emergence (PRE or POST). In a field PRE study, the 90% effective doses (ED90) were 2613 and 2863 g a.i. ha−1 at 14 and 35 d after treatment (DAT), respectively, which were comparable to the labeled rate (2900 g a.i. ha−1) of the premix. Under greenhouse conditions, POST applications made at the labeled rate to 8–10 or 15–18 cm diameter horseweed rosettes provided ≥97% control. The ED90 values for the in-field POST dose-response study were ≥3431 and ≥6717 g a.i. ha−1 for the 8–10 and 15–18 cm tall GR horseweed, respectively. At 14 DAT, the premix applied at the labeled rate provided 85% and 68% control of 8–10 and 15–18 cm tall GR horseweed, respectively. The root mean square error for the log-logistic model ranged from 4.2 to 9.2 and the model efficiency coefficient values were ≥0.94 (≈ 1.00), indicating a good fit for the prediction model. In conclusion, a new premix applied before emergence (PRE) will effectively control GR horseweed at the labeled rate compared with POST applications made to ≥8 cm tall plants.

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)0.001
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 score0.976
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.223
Teacher spread0.209 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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