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Record W2015239124 · doi:10.4236/as.2013.412091

Effect of imazethapyr application timing in kidney and white bean

2013· article· en· W2015239124 on OpenAlexafffundabout
Nader Soltani, Robert E. Nurse, Peter H. Sikkema

Bibliographic record

VenueAgricultural Sciences · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed Germination and Physiology
Canadian institutionsUniversity of Guelph
FundersAgriculture and Agri-Food CanadaAgricultural Adaptation Council
KeywordsShootDry beanCropWater contentDry weightAgronomyKidneyHorticultureAnimal scienceBiologyPhaseolus

Abstract

fetched live from OpenAlex

A total of twelve field trials (6 with kidney bean and 6 with white bean) were conducted over a three-year period (2010 to 2012) at various locations in Ontario to evaluate the tolerance of kidney and white bean to imazethapyr applied preplant incorporated (PPI), preemergence (PRE), and postemergence (POST) at 37.5, 75 and 150 g·ai·ha-1. Imazethapyr applied PPI or PRE in kidney or white bean caused 0%-1%, 0%-4%, and 0%-9% injury at 1, 2, and 4 weeks after application (WAA), respectively. However, the injury was transient and had no adverse effect on shoot dry weight, height, seed moisture content, and yield of kidney or white bean except for shoot dry weight which was reduced 32% with imazethapyr applied PPI compared to the weed free control in white bean. Imazethapyr applied POST in kidney or white bean caused as much as 17%, 18%, and 11% injury at 1, 2, and 4 WAA, respectively. There was no adverse effect on shoot dry weight, height, seed moisture content, and yield of kidney or white bean except for imazethapyr applied POST which increased seed moisture content 1.9% compared to the weed free control in white bean. Based on these results, there is an adequate margin of crop safety in dry bean to imazethapyr applied PPI or PRE at 37.5 or 75 g·ai·ha-1 in kidney and white beans. However, there is not an adequate margin of crop safety for imazethapyr applied POST at rates higher than 37.5 g·ai·ha-1 in kidney and white bean.

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 score0.924
Threshold uncertainty score0.153

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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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

Citations1
Published2013
Admission routes3
Has abstractyes

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