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Record W2316238278 · doi:10.4141/cjps10065

Control of common cocklebur (<i>Xanthium strumarium</i> L.) in corn

2010· article· en· W2316238278 on OpenAlexvenueaboutno aff
Nader Soltani, Christy Shropshire, Peter H. Sikkema

Bibliographic record

VenueCanadian Journal of Plant Science · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMesotrioneDicambaAtrazineAgronomyWeed controlBiologyChemistryPesticide

Abstract

fetched live from OpenAlex

Nine field trials (five with PRE and four with POST herbicides) were conducted in 2006 to 2009 on various Ontario farms with heavy common cocklebur infestations to determine the effectiveness of PRE and POST herbicides for the control of common cocklebur in corn. There was no commercially significant corn injury from the PRE herbicides evaluated. Saflufenacil, saflufenacil/dimethenamid-p, isoxaflutole + atrazine, mesotrione + atrazine and dicamba/atrazine, applied PRE provided 85, 85, 76, 73 and 67% control of common cocklebur in corn 8 wk after emergence (WAE), respectively. Common cocklebur shoot dry weight was reduced 84, 80, 79, 75 and 68% with saflufenacil/dimethenamid-p, isoxaflutole + atrazine, mesotrione + atrazine, saflufenacil and dicamba/atrazine, respectively. There was no effect on corn yield compared with the weedy control with the PRE herbicides evaluated. The application of 2,4-D/atrazine POST resulted in unacceptable injury (28%) in corn. Dicamba/atrazine, dicamba/diflufenzopyr, dicamba and mesotrione + atrazine provided up to 98, 95, 90 and 90% control of common cocklebur 8 wk after application (WAA), respectively. All POST herbicide treatments increased corn yield compared with the non-treated control. Saflufenacil and saflufenacil/dimethenamid-p applied PRE and dicamba, dicamba/diflufenzopyr, dicamba/atrazine or mesotrione + atrazine applied POST have potential to provide good to excellent control of common cocklebur in corn 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 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 score0.925
Threshold uncertainty score0.949

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 science0.0010.000
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 head0.009
GPT teacher head0.195
Teacher spread0.186 · 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

Citations9
Published2010
Admission routes2
Has abstractyes

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