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Record W2141073712 · doi:10.1614/wt-04-219r2.1

Evaluation of Glyphosate-Resistant Hard Red Spring Wheat (<i>Triticum aestivum</i>)

2006· article· en· W2141073712 on OpenAlexaboutno aff
Kirk Howatt, Gregory Endres, Paul E. Hendrickson, Ezra Aberle, John R. Lukach, Brian Jenks, N. R. Riveland, Stephen A. Valenti, Craig M. Rystedt

Bibliographic record

VenueWeed Technology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsnot available
Fundersnot available
KeywordsGlyphosateFoxtailDicambaAgronomyLambsquartersBiologyWeed controlBromoxynilWeedSetaria viridisChenopodium

Abstract

fetched live from OpenAlex

The potential for future commercialization of glyphosate-resistant wheat necessitates evaluation of agronomic merits of this technology. Experiments were established to evaluate glyphosate-resistant wheat and weed responses to glyphosate rate, application timing, and tank mixtures. Glyphosate at 1,680 g/ha did not injure wheat. Wheat response to glyphosate applied to one- to three- or three- to five-leaf wheat was not different from that of untreated wheat. Wheat was injured more from glyphosate plus thifensulfuron or glyphosate plus dicamba than from individual herbicides at one of six locations, but grain yield was not affected by glyphosate tank mixtures. Glyphosate application timing did not affect control of wild oat or common lambsquarters 56 d after treatment. Glyphosate when applied to one- to three-leaf wheat provided better control of wild buckwheat than later glyphosate application, whereas glyphosate applied to three- to five-leaf wheat provided the best control of green and yellow foxtail, redroot pigweed, and Canada thistle. Weed control with glyphosate tended to be better than with conventional herbicides, and wheat treated with glyphosate produced approximately 10% more grain than wheat treated with conventional herbicide tank mixes.

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 score0.806
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
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.226
Teacher spread0.207 · 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
Published2006
Admission routes1
Has abstractyes

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