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Record W1968659636 · doi:10.1155/2014/950923

Weed Management in Spring Seeded Barley, Oats, and Wheat with Prosulfuron

2014· article· en· W1968659636 on OpenAlexafffundabout
Nader Soltani, Lynette R. Brown, Todd Cowan, Peter H. Sikkema

Bibliographic record

VenueInternational Journal of Agronomy · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of Guelph
FundersGrain Farmers of Ontario
KeywordsGlyphosateAgronomyWeed controlCropBiomass (ecology)WeedSeedingBiologyEnvironmental science

Abstract

fetched live from OpenAlex

A limited number of preplant (PP) herbicides are available for spring seeded cereals in Ontario. Six field trials were conducted at the Huron Research Station, Exeter, Ontario, over a two-year period (Exeter, 2010 and 2011) to evaluate glyphosate, prosulfuron, and glyphosate plus prosulfuron applied PP for weed management in spring seeded no-till barley, oats, and wheat. There was no injury in barley, oats, and wheat with glyphosate, prosulfuron, and glyphosate plus prosulfuron applied preplant at the rates evaluated at 1, 2, and 4 weeks after crop emergence. Prosulfuron provided 49–99% control of AMBEL, 28% or less control of CONAR, 31–94% control of POLCO, 49–98% control of SINAR, and 46–79% control of SONAR. Prosulfuron in combination with glyphosate provided 73–98% control of AMBEL, less than 43% control of CONAR, 39–94% control of POLCO, 63–98% control of SINAR, and 60–85% control of SONAR. Prosulfuron reduced density of AMBEL 76% and SINAR 93% but had no significant effect on density of CONAR, POLCO, or SONAR. Prosulfuron in combination with glyphosate reduced biomass of AMBEL as much as 96% and SINAR 98% but had no significant effect on biomass of CONAR, POLCO, or SONAR. Yield of barley, oats, and wheat was not affected with glyphosate, prosulfuron, and glyphosate plus prosulfuron at the rates evaluated.

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 score0.875
Threshold uncertainty score0.095

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 head0.006
GPT teacher head0.200
Teacher spread0.193 · 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

Citations5
Published2014
Admission routes3
Has abstractyes

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