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Record W2431375278 · doi:10.4236/ajps.2016.78117

Response of Four Dry Bean Market Classes to Pre-Emergence Applications of Pyroxasulfone, Sulfentrazone and Pyroxasulfone plus Sulfentrazone

2016· article· en· W2431375278 on OpenAlexaffabout
Allison N. Taziar, Nader Soltani, Christy Shropshire, Darren E. Robinson, Mitch Long, Chris L. Gillard, Peter H. Sikkema

Bibliographic record

VenueAmerican Journal of Plant Sciences · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBiologyAgronomyMathematics

Abstract

fetched live from OpenAlex

Only one herbicide mode of action (ALS inhibitor) is currently available to Ontario dry bean producers for soil-applied broadleaf weed control. Four field studies were conducted over two years (2014, 2015) to examine the tolerance of four market classes of dry beans to sulfentrazone (210 and 420 g·ai·ha-1) and pyroxasulfone (100 and 200 g·ai·ha-1) applied alone and in combination. The registration of these two herbicides would provide Ontario dry bean producers with two additional modes of action for broadleaf weed control. Pyroxasulfone caused up to 23%, 6%, 7% and 10% injury in adzuki, kidney, small red Mexican and white bean, respectively; sulfentrazone caused up to 51%, 12%, 15% and 44% injury and the combination caused up to 90%, 23%, 29% and 62% injury, respectively. Kidney and small red Mexican bean density, height, seed moisture content and yield were not affected. Pyroxasulfone (200 g·ai·ha-1) + sulfentrazone (420 g·ai·ha-1) reduced adzuki and white bean density, shoot dry weight, height and yield. This study concludes that pyroxasulfone (100 g·ai·ha-1) + sulfentrazone (210 g·ai·ha-1) applied PRE can be safely used to control weeds in Ontario kidney and small red Mexican bean production.

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)0.002
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 score0.952
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.236
Teacher spread0.220 · 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

Citations10
Published2016
Admission routes2
Has abstractyes

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