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Record W6922130371 · doi:10.1139/cjps-2015-064

Tolerance of adzuki bean to pre-emergence herbicides

2015· article· en· W6922130371 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsnot available
Fundersnot available
KeywordsShootWeedCropWeed controlCrop yieldYield (engineering)

Abstract

fetched live from OpenAlex

Soltani, N., Nurse, R. E., Shropshire, C. and Sikkema, P. H. 2015. Tolerance of adzuki bean to pre-emergence herbicides. Can. J. Plant Sci. 95: 959-963. Limited pre-emergence herbicide options are available for weed management in adzuki bean in Ontario. Eight field trials were conducted in Ontario over a 3-yr period (2012, 2013, 2014) to evaluate the tolerance of adzuki bean to pyroxasulfone (150 and 300 g a.i. ha-1), flumioxazin (71 and 142 g a.i. ha-1), sulfentrazone (420 and 840 g a.i. ha-1), fomesafen (240 and 480 g a.i. ha-1), imazethapyr (75 and 150 g a.i. ha-1), and cloransulam-methyl (35 and 70 g a.i. ha-1) applied pre-emergence. Pyroxasulfone, flumioxazin and sulfentrazone applied pre-emergence at the proposed 1× and 2× rates caused 25-96% injury and reduced plant stand up to 78%, shoot dry weight up to 95%, plant height up to 67% and seed yield up to 76% in adzuki bean. Cloransulam-methyl resulted in 1 to 9% injury with no adverse effect on plant stand, shoot dry weight, plant height, seed moisture content and seed yield of adzuki bean. Fomesafen and imazethapyr resulted in 1-3% injury with no adverse effect on plant stand, shoot dry weight, plant height, seed moisture content and seed yield of adzuki bean. Based on these results, pyroxasulfone, flumioxazin and sulfentrazone do not have an adequate margin of crop safety for weed management in adzuki bean. Cloransulam-methyl has potential for use in adzuki bean, especially at the lower rate. Imazethapyr and fomesafen at the rates evaluated can be used safely in adzuki bean production under Ontario environmental conditions.

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 score0.947
Threshold uncertainty score0.822

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 science0.0010.000
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 head0.312
GPT teacher head0.258
Teacher spread0.054 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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