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Record W1998382248 · doi:10.1155/2014/391634

Weed Control in White Bean with Various Halosulfuron Tankmixes

2014· article· en· W1998382248 on OpenAlexafffundabout
Nader Soltani, Robert E. Nurse, Christy Shropshire, Peter H. Sikkema

Bibliographic record

VenueAdvances in Agriculture · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
FundersAgricultural Adaptation Council
KeywordsLambsquartersFoxtailTrifluralinChenopodiumRagweedWeed controlAgronomyMetolachlorBiologyWeedHorticultureDandelionPesticideAtrazineMedicine

Abstract

fetched live from OpenAlex

Four field trials were conducted over a three-year period (2011–2013) in southwestern Ontario to evaluate the level of weed control provided by various halosulfuron tankmixes applied preplant incorporated (PPI) in white bean. Trifluralin, s-metolachlor, halosulfuron, and imazethapyr applied alone or in combination caused 4% or less visible injury 1 and 4 weeks after emergence (WAE) in white bean. Trifluralin, s-metolachlor, halosulfuron, and imazethapyr applied PPI provided 80–96%, 84–95%, 83–100%, and 75–92% control of redroot pigweed; 19–28%, 30–40%, 97–99%, and 73–84% control of common ragweed; 94–96%, 63–82%, 96–100%, and 96–100% control of common lambsquarters; 14-15%, 12–35%, 100%, and 96–97% control of wild mustard; and 96–97%, 95–97%, 53–56%, and 80–82% control of green foxtail, respectively. The two- and three-way tankmixes of halosulfuron with trifluralin, s-metolachlor, or imazethapyr provided 85–100% control of redroot pigweed, 90–98% control of common ragweed, 97–100% control of common lambsquarters, 100% control of wild mustard, and 93–98% control of green foxtail. Weed density, weed biomass and white bean seed yields reflected the level of visible weed control.

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 score0.598
Threshold uncertainty score0.973

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 head0.003
GPT teacher head0.187
Teacher spread0.184 · 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

Citations17
Published2014
Admission routes3
Has abstractyes

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