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Record W3176181566 · doi:10.1017/wet.2021.46

Biologically effective dose of pyraflufen-ethyl/2,4-D, applied preplant alone or mixed with metribuzin on glyphosate-resistant horseweed in soybean

2021· article· en· W3176181566 on OpenAlexaffabout
David B. Westerveld, Nader Soltani, David C. Hooker, Darren E. Robinson, Peter H. Sikkema

Bibliographic record

VenueWeed Technology · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMetribuzinGlyphosateHerbicide resistanceBiologyWeed controlAgronomy

Abstract

fetched live from OpenAlex

Abstract Glyphosate resistance in weed species has presented immense challenges for farmers in Ontario. The co-application of burndown plus residual herbicides provides control of glyphosate-resistant (GR) horseweed in soybean. Pyraflufen-ethyl/2,4-D is a premixed herbicide formulation sold under the tradename Blackhawk ® . Five field experiments were conducted over a 2-yr period (2019, 2020) in fields in southwestern Ontario to ascertain the biologically effective dose of pyraflufen-ethyl/2,4-D, applied alone, or mixed with metribuzin, for GR horseweed control when applied preplant to soybean. Soybean visible injury for all treatments was <15%. At 8 wk after application (WAA), the calculated doses of pyraflufen-ethyl/2,4-D for 50%, 80%, and 95% control of GR horseweed were 390, 1,148, and >2,108 g ha −1 , respectively. The addition of metribuzin to pyraflufen-ethyl/2,4-D reduced the doses of pyraflufen-ethyl/2,4-D for 50%, 80%, and 95% control of GR horseweed to 19, 46, and 201 g ha −1 , respectively. Pyraflufen-ethyl/2,4-D + metribuzin controlled GR horseweed by 97%, which is comparable to the current industry standards. Based on these results, pyraflufen-ethyl/2,4-D + metribuzin (527 + 400 g ha −1 ) applied preplant can be used for GR horseweed control in soybean.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.010
GPT teacher head0.210
Teacher spread0.201 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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