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Record W6959598407 · doi:10.1139/cjps-2014-355

Length of residual activity of saflufenacil/dimethenamid-p in soybean (Glycine max)

2015· article· en· W6959598407 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCocoa and Sweet Potato Agronomy
Canadian institutionsnot available
Fundersnot available
KeywordsRagweedResidualAmbrosia artemisiifoliaWeedLambsquarters

Abstract

fetched live from OpenAlex

Soltani, N., Robinson, D. E, Miller, R. T. and Sikkema, P. H. 2015. Length of residual activity of saflufenacil/dimethenamid-p in soybean (Glycine max). Can. J. Plant Sci. 95: 727-733. A total of six field studies were conducted over a 2-yr period (2009, 2010) at three Ontario locations to determine the length of residual activity of saflufenacil/dimethenamid-p applied preemergence (PRE) for the control of annual weeds in soybean. As the rate of saflufenacil/dimethenamid-p increased from 0 to 980 g a.i. ha-1 the maximum cumulative emergence percentage for common lambsquarters, redroot pigweed and common ragweed decreased. Additionally, as the saflufenacil/dimethenamid-p rate increased from 0 to 980 g a.i. ha-1, the cumulative emergence of common lambsquarters, redroot pigweed and common ragweed slowed down over time. Times to 10% emergence were 1.0, 1.0, 2.1, 3.2, 3.9, 6.6 and >12 wk for common lambsquarters, 1.4, 1.6, 1.8, 2.6, 4.3, >12 and >12 wk for redroot pigweed and 0.8, 1.0, 2.2, 2.7, 3, >12 and >12 wk for common ragweed with saflufenacil/dimethenamid-p applied at 0, 30.625, 61.25, 122.5, 245, 490 and 980 g a.i. ha-1, respectively. Based on these results, saflufenacil/dimethenamid-p applied PRE at the labelled use rate of 245 g a.i. ha-1 can provide as much as 3.9, 4.3 and 3wk of effective control of common lambsquarters, redroot pigweed and common ragweed, respectively in soybean.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.395
GPT teacher head0.251
Teacher spread0.143 · 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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