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Record W1975064039 · doi:10.4236/ajps.2012.31009

Response of Eight Sweet Maize (<i>Zea mays</i> L.) Hybrids to Saflufenacil Alone or Pre-Mixed with Dimethenamid-P

2012· article· en· W1975064039 on OpenAlexaffabout
Darren E. Robinson, Nader Soltani, Peter H. Sikkema

Bibliographic record

VenueAmerican Journal of Plant Sciences · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsZea maysHybridAgronomyWeedWeed controlBiologyChemistryHorticulture

Abstract

fetched live from OpenAlex

Saflufenacil is a new herbicide for use in field maize (Zea mays L.) and other crops that may have potential for weed management in sweet maize. Tolerance of eight sweet maize hybrids to saflufenacil and saflufenacil plus dimethenamid-p applied preemergence (PRE) were studied at two Ontario locations in 2008 and 2009. Saflufenacil applied PRE at 75 and 150 g·ha–1 and saflufenacil plus dimethenamid-p (pre-mixed) applied PRE at 735 and 1470 g·ha–1 caused minimal (less than 5%) injury in Cahill, GH4927, Harvest Gold, Rocker, BSS5362, GG236, GG447, and GG763 sweet maize hybrids at 1 and 2 weeks after emergence (WAE). Saflufenacil or saflufenacil plus dimethenamid-p applied PRE did not reduce plant height, cob size, or yield of any of the sweet maize hybrids tested in this study. Based on these results, saflufenacil and saflufenacil plus dimethenamid-p pre-mixed applied PRE at the doses evaluated can be safely used for weed management in Cahill, GH4927, Harvest Gold, Rocker, BSS5362, GG236, GG447, and GG763 sweet maize under Ontario environmental conditions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

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 head0.034
GPT teacher head0.261
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2012
Admission routes2
Has abstractyes

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