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Record W1935736718 · doi:10.4141/cjps-2014-351

Tolerance of identity preserved soybean cultivars to preemergence herbicides

2015· article· en· W1935736718 on OpenAlexafffundvenueabout
Kimberly D. Belfry, Nader Soltani, Lynette R. Brown, Peter H. Sikkema

Bibliographic record

VenueCanadian Journal of Plant Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of Guelph
FundersGrain Farmers of Ontario
KeywordsMetribuzinCultivarClomazoneBiologyMetolachlorAgronomyHorticultureWeed controlPesticideAtrazine

Abstract

fetched live from OpenAlex

Belfry, K. D., Soltani, N., Brown, L. R. and Sikkema, P. H. 2015. Tolerance of identity preserved soybean cultivars to preemergence herbicides. Can. J. Plant Sci. 95: 719–726. Six field trials were conducted over a 3-yr period (2011 to 2013) near Exeter and Ridgetown, Ontario, Canada, to assess the tolerance of seven identity-preserved (IP) soybean cultivars to preemergence (PRE) herbicides applied at 2×the recommended dose. All cultivars demonstrated excellent tolerance to dimethenamid-P, pyroxasulfone, S-metolachlor, chlorimuron-ethyl, clomazone, cloransulam-methyl, flumetsulam and imazethapyr, showing 5% or less injury. At Exeter, linuron caused as much as 13 and 18% injury at 2 and 4 wk after soybean emergence, while injury due to metribuzin was as high as 22 and 47% for the same dates, respectively. At Exeter, linuron, metribuzin and S-metolachlor plus metribuzin reduced soybean height up to 17, 41 and 24%, respectively; soybean height was generally not affected at the Ridgetown sites. At Exeter, metribuzin reduced soybean yield up to 38% for cultivars S03W4, Madison and OAC Lakeview, while S23T5 was not significantly reduced. On the contrary, metribuzin yield loss at Ridgetown was nonsignificant. Results from this study suggest that PRE application of metribuzin at 2× field dose has potential to cause unacceptable injury and yield reduction in IP soybeans under some soil and climatic conditions and may vary according to cultivar.

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 score0.945
Threshold uncertainty score0.955

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 communication0.0000.001
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 head0.042
GPT teacher head0.248
Teacher spread0.206 · 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

Citations24
Published2015
Admission routes4
Has abstractyes

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