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Record W2908511645 · doi:10.1155/2018/2402696

Weed Control in White Bean with Pethoxamid Tank-Mixes Applied Preemergence

2018· article· en· W2908511645 on OpenAlexafffundabout
Nader Soltani, Lynette R. Brown, Peter H. Sikkema

Bibliographic record

VenueInternational Journal of Agronomy · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of Guelph
FundersAgricultural Adaptation Council
KeywordsLambsquartersWeed controlFoxtailAgronomyWeedMetolachlorBiologyRagweedChenopodiumPesticideAtrazine

Abstract

fetched live from OpenAlex

Six field experiments were conducted during 2015 to 2017 in Ontario, Canada, to determine the efficacy of pethoxamid applied alone, and in combination with broadleaf herbicides, for the control of annual grass and broadleaved weeds in white navy bean. Visible injury was generally minimal (0 to 8%) with herbicide treatments evaluated. Weed control was variable depending on the weed species evaluated. Pethoxamid, S -metolachlor, halosulfuron, imazethapyr, sulfentrazone, pethoxamid + halosulfuron, pethoxamid + imazethapyr, and pethoxamid + sulfentrazone controlled redroot pigweed 82 to 98%; common ragweed 19 to 93%; common lambsquarters 49 to 84%; and green foxtail 47 to 92% in white bean. Weed biomass and weed density reductions were similar to visible control ratings for herbicides evaluated. Weed interference delayed white bean maturity and reduced yield by 50% in this study. Weed interference in plots sprayed with pethoxamid, S -metolachlor, and sulfentrazone reduced white bean yield 36%. White bean yield was similar to the weed-free with other herbicides evaluated. This study concludes that there is potential for the tank-mix of pethoxamid with halosulfuron, imazethapyr, or sulfentrazone for weed control in white 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)0.000
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 score0.840
Threshold uncertainty score0.817

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)0.0010.000

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 head0.009
GPT teacher head0.219
Teacher spread0.211 · 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

Citations3
Published2018
Admission routes3
Has abstractyes

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