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Record W6884622339 · doi:10.1139/cjps2012-181

Winter wheat (Triticum aestivum L.) tolerance to mixtures of herbicides and fungicides applied at different timings

2013· article· en· W6884622339 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsnot available
Fundersnot available
KeywordsTebuconazoleFungicideWinter wheatPesticideField experiment

Abstract

fetched live from OpenAlex

Robinson, M. A., Cowbrough, M. J., Sikkema, P. H. and Tardif, F. J. 2013. Winter wheat (Triticum aestivum L.) tolerance to mixtures of herbicides and fungicides applied at different timings. Can. J. Plant Sci. 93: 491-501. Farmers commonly tank-mix herbicides and fungicides to reduce application costs. In the spring of 2008, there were reports of winter wheat injury with the application of herbicide-fungicide tank-mixes early in the growing season. This study was established to determine the tolerance of winter wheat to herbicide-fungicide mixtures as influenced by time of application. Field studies were conducted at four Ontario locations in 2009 and 2010 with three herbicides and four fungicides. Herbicide-fungicide tank-mixes were applied early, under cold conditions, and late at growth stage Zadoks 37-39. Dichlorprop/2,4-D mixed with tebuconazole caused up to 15% injury when applied early and up to 29% injury when applied late. Bromoxynil/MPCA mixed with tebuconazole injured wheat up to 15% when applied early but only 10% when applied late. Other herbicide and fungicide mixes caused a lower level of injury. Visible injury was transient and did not reduce winter wheat yields. The likelihood of tank-mixes causing injury was greater when they were applied late. The fungicide tebuconazole caused the highest level of injury when mixed with herbicides and injury was particularly high with dichlorprop/2,4-D.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.997

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)0.0010.000

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 head0.125
GPT teacher head0.215
Teacher spread0.090 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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