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Record W2619509684 · doi:10.1139/cjps-2017-0023

Effect of soybean and winter wheat herbicides on oilseed radish establishment and growth

2017· article· en· W2619509684 on OpenAlexafffundvenueabout
Taïga B. Cholette, Nader Soltani, David C. Hooker, Darren E. Robinson, Peter H. Sikkema

Bibliographic record

VenueCanadian Journal of Plant Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of Guelph
FundersBayer CropScienceGrain Farmers of Ontario
KeywordsRaphanusAgronomyBiologyBiomass (ecology)Weed controlGlyphosateWeedWinter wheatCropGrowing seasonBrassica

Abstract

fetched live from OpenAlex

Residual herbicides can cause injury to a future crop. Residual herbicides applied to soybean (Glycine max L. Merr.) or winter wheat (Triticum aestivum L.) may impact cover crops seeded in the same growing season. The objective of this study was to determine the effect of residual herbicides on oilseed radish (Raphanus sativus L.) establishment and growth after 10 postemergence (POST) broadleaf herbicides were applied in winter wheat, and 12 preemergence (PRE) and 7 POST herbicides were applied in soybean. Oilseed radish injury was assessed 14 and 28 days after emergence (DAE) and stand density and biomass were measured 28 DAE. Residual herbicides used for broadleaf weed control in winter wheat caused less than 5% oilseed radish injury and there was no reduction in stand density and biomass. In soybean, imazethapyr applied PRE caused 43 and 48% oilseed radish injury at 14 and 28 DAE, respectively. There was no decrease in oilseed radish stand density and biomass. Imazethapyr applied POST to soybean caused 47 and 59% oilseed radish injury at 14 and 28 DAE, respectively, and decreased oilseed radish biomass by 65%. There was no decrease in oilseed radish stand density. The results from this study conclude that many of the herbicides commonly used in winter wheat and soybean in Ontario do not negatively impact oilseed radish establishment and growth.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.009
GPT teacher head0.212
Teacher spread0.202 · 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

Citations3
Published2017
Admission routes4
Has abstractyes

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