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Record W1976712788 · doi:10.4236/ajps.2014.51020

Weed Management in Spring Planted Cereals with Mesotrione

2014· article· en· W1976712788 on OpenAlexafffundabout
Nader Soltani, Christy Shropshire, Todd Cowan, Peter H. Sikkema

Bibliographic record

VenueAmerican Journal of Plant Sciences · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of Guelph
FundersGrain Farmers of Ontario
KeywordsMesotrioneWeed controlAgronomyWeedYield (engineering)BiologyAtrazinePesticide

Abstract

fetched live from OpenAlex

There is little information on the efficacy of mesotrione for the control of broadleaved weeds in spring planted cereals under Ontario environmental conditions. A total of eight studies were conducted in Ontario over a two-year period (2010 to 2011) to evaluate cereal tolerance and weed control efficacy of mesotrione applied preemergence (PRE) at 25, 50, 100, 140, and 280 g ai ha-1 in spring planted barley, durum wheat, oats, and wheat. Mesotrione, applied preemergence at the rates evaluated, caused no injury in either year in spring planted barley, durum wheat, oats, or wheat evaluated at 1, 2 and 4 week after emergence (WAE). The predicted mesotrione rate required to give adequate control of AMBEL, CHEAL, POLCO and SINAR was generally greater than 280 g ai ha-1. The average yield of the weedy check was 81% of the weed-free check. According to the exponential to maximum regression, the mesotrione rates required to give 90%, 95% and 98% of the weed-free check were 15, 30 and 45 g ai ha-1, respectively. To provide yield equivalent to the standard treatment of bromoxynil/MCPA, 36 g ai ha-1 of mesotrione was needed. Based on these results, mesotrione applied preemergence at 25, 50, 100, 140, and 280 g ai ha-1 can be safely used in spring planted barley, durum wheat, oats, and wheat. However, greater than 280 g ai ha-1 of mesotrione was needed to adequately control AMBEL, CHEAL, POLCO and SINAR.

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)0.001
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 score0.892
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.209
Teacher spread0.196 · 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

Citations9
Published2014
Admission routes3
Has abstractyes

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