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Record W2133765583 · doi:10.21273/hortsci.47.8.1159

Combining Preemergence Herbicides in Tank Mixtures or as Sequential Applications Provides Season-long Crabgrass Control in the Upper Midwest

2012· article· en· W2133765583 on OpenAlexaboutno aff
Christopher A. Proctor, Matt Sousek, Aaron J. Patton, Daniel V. Weisenberger, Zachary J. Reicher

Bibliographic record

VenueHortScience · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPendimethalinQuarter (Canadian coin)MathematicsDigitariaWeed controlAgronomyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Preemergence (PRE) herbicides are used to control crabgrass ( Digitaria spp.). Single spring applications are common in areas with relatively low crabgrass pressure, whereas sequential applications often are used to extend control in locations with high crabgrass pressure. Our objectives were to determine if changing a.i. in initial and sequential applications affects crabgrass control and if single spring applications of tank-mixed PRE herbicides provide season-long crabgrass control. Studies were conducted 2009, 2010, and 2011 in West Lafayette, IN, and 2011 in Wymore, NE. The PRE herbicides prodiamine, pendimethalin, and dithiopyr were tested using different application strategies. Sequential applications were applied mid-April and mid-June using all possible combinations of the three herbicides and untreated for the initial and sequential application. These herbicides also were applied mid-April as single full-rate PRE application or as a tank mixture of two PRE herbicides at half-plus-half or half-plus-quarter rate. Season-long crabgrass control was consistently achieved using sequential applications regardless which of the three herbicides was used for initial or sequential applications. Single applications of tank mixtures also provided consistent crabgrass control, equivalent to single full-rate applications of the individual PRE herbicides. Tank mixtures of half-plus-quarter rate and single half-rate applications resulted in more crabgrass cover than single full-rate or half-plus-half rate applications regardless of the herbicide applied.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.023
GPT teacher head0.269
Teacher spread0.245 · 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
Published2012
Admission routes1
Has abstractyes

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