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Record W2757983593 · doi:10.1017/wet.2017.67

Potential Benefit and Risk of Fluridone as a Fall Germination Stimulant in Western Canada

2017· article· en· W2757983593 on OpenAlexaffabout
Breanne D. Tidemann, Linda M. Hall, K. Neil Harker, Hugh J. Beckie

Bibliographic record

VenueWeed Technology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsNational Association of Friendship CentresAgriculture and Agri-Food CanadaAlberta Ministry of Agriculture and ForestryUniversity of Alberta
Fundersnot available
KeywordsFluridoneWeedWeed controlGlyphosateAgronomyGerminationCanolaGlufosinateBiologyAbscisic acid

Abstract

fetched live from OpenAlex

Herbicide resistance has increased the need for novel weed control strategies. Fluridone has herbicidal as well as potential germination stimulant activity. The objectives of this study were to evaluate fluridone as a fall-applied germination stimulant for weed control and to assess rotational crop tolerance. Fall-applied fluridone was compared with a nontreated control in areas established with false cleavers, volunteer canola, and wild oat at Lacombe, AB, in 2014–2015 and 2015–2016, and at St Albert, AB, in 2015–2016. In the fall, there was a trend for weed densities to be higher in fluridone treatments than in untreated controls across site-years. The stimulatory effect of fluridone on weed germination was not statistically significant in fall assessments, while the weed control effect was significant in 33% of spring assessments. While fluridone reduced weed biomass for some site-years, it also reduced canola crop emergence and biomass at St Albert in 2015–2016, and caused injury symptoms on wheat and field pea. Risk of carryover to subsequent crops outweighed the benefits of using fluridone in the fall to stimulate weed germination in this study.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.143

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 head0.206
Teacher spread0.200 · 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 routes2
Has abstractyes

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