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Record W2195308321 · doi:10.4141/cjps-2014-124

Additive efficacy of soil-applied pyroxasulfone and sulfentrazone combinations

2014· article· en· W2195308321 on OpenAlexaffvenueabout
Breanne D. Tidemann, Linda M. Hall, Eric N. Johnson, Hugh J. Beckie, Ken L. Sapsford, Christian J. Willenborg, Lisa L. Raatz

Bibliographic record

VenueCanadian Journal of Plant Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsAvena fatuaAgronomyBiologyWeed controlWeedCanolaSoil waterEcology

Abstract

fetched live from OpenAlex

Tidemann, B. D., Hall, L. M., Johnson, E. N., Beckie, H. J., Sapsford, K. L., Willenborg, C. J. and Raatz, L. L. 2014. Additive efficacy of soil-applied pyroxasulfone and sulfentrazone combinations. Can. J. Plant Sci. 94: 1245–1253. Efficacy of soil-applied herbicides can be influenced by edaphic factors including soil organic matter (OM) content, as well as by interactions with herbicide tank-mix partners. Field trials were conducted over 6 site-years in 2011 and 2012 across western Canada to examine the interaction of pyroxasulfone and sulfentrazone when co-applied for control of false cleavers (Galium spurium L.) and wild oat (Avena fatua L.) in field pea. In the greenhouse, the nature of this interaction was further investigated for these two weed species, plus barley and canola; in a separate experiment, the effect of OM content on pyroxasulfone and sulfentrazone efficacy was examined using three soils with 2.8, 5.5, and 12.3% OM content, respectively. Efficacy of pyroxasulfone and sulfentrazone combinations was additive under both field and greenhouse conditions. Higher OM content generally required higher rates of herbicide to achieve similar efficacy for all tested species. Pyroxasulfone and sulfentrazone can be combined to aid in herbicide resistance management and broaden the weed spectrum compared with each product used alone, although rate selection may be OM dependent.

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 score0.978
Threshold uncertainty score0.995

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 head0.009
GPT teacher head0.185
Teacher spread0.176 · 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

Citations16
Published2014
Admission routes3
Has abstractyes

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