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Record W2049259838 · doi:10.1155/2014/287908

Response of Dry Bean to Sulfentrazone Plus Imazethapyr

2014· article· en· W2049259838 on OpenAlexafffundabout
Nader Soltani, Christy Shropshire, Peter H. Sikkema

Bibliographic record

VenueInternational Journal of Agronomy · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of Guelph
FundersAgricultural Adaptation Council
KeywordsDry beanDry weightCropHorticultureAgronomyBiologyPhaseolus

Abstract

fetched live from OpenAlex

Field studies were conducted in 2010 and 2011 at the Huron Research Station, Exeter, Ontario and from 2009 to 2011 at the University of Guelph Ridgetown Campus, Ridgetown, Ontario to evaluate the sensitivity of four market classes of dry bean to sulfentrazone applied preemergence at 105, 140, and 280 g ai/ha alone and in combination with imazethapyr at 37.5 g ai/ha. At 1 week after emergence (WAE), sulfentrazone alone or in combination with imazethapyr at all doses evaluated caused no significant visible injury in dry bean. At 2 WAE, sulfentrazone alone caused 1–11, 1–11, 1–5, and 3–19% visible injury, and sulfentrazone + imazethapyr caused 3–11, 2–10, 2–5, and 4–20% visible injury in black, cranberry, kidney, and white bean, respectively. At 4 WAE, sulfentrazone alone caused 1–7, 1–7, 0–4, and 1–16% visible injury and sulfentrazone + imazethapyr caused 1–8, 1–5, 1–3, and 2–14% visible injury in black, cranberry, kidney, and white bean, respectively. Sulfentrazone PRE caused slightly greater injury in black and white bean compared to cranberry and kidney bean. Generally, crop injury with sulfentrazone at rates up to 140 g ai/ha alone and in combination with imazethapyr at 37.5 g ai/ha was minimal with no adverse effect on plant height, shoot dry weight, seed moisture content, and yield. Based on these results, there is potential for preemergence application of sulfentrazone at rates up to 140 g ai/ha alone or in combination with imazethapyr at 37.5 g ai/ha in black, cranberry, 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)0.001
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 score0.913
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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

Citations8
Published2014
Admission routes3
Has abstractyes

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