MétaCan
Menu
Back to cohort
Record W2086558729 · doi:10.1614/wt-05-144.1

Tolerance of Otebo Bean (<i>Phaseolus vulgaris</i>) to New Herbicides in Ontario

2006· article· en· W2086558729 on OpenAlexaffabout
Peter H. Sikkema, Darren E. Robinson, Christy Shropshire, Nader Soltani

Bibliographic record

VenueWeed Technology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTrifluralinMetolachlorPhaseolusShootDry beanAgronomyDry weightWeed controlWeedHorticultureCropBiologyPesticideAtrazine

Abstract

fetched live from OpenAlex

Weed management is a major production issue facing otebo bean growers in Ontario. Field trials were conducted at six Ontario locations during a 2-yr period (2003 and 2004) to evaluate the tolerance of otebo bean to the preplant incorporated (PPI) application of EPTC at 4,400 and 8,800 g ai/ha, trifluralin at 1,155 and 2,310 g ai/ha, dimethenamid at 1,250 and 2,500 g ai/ha, S -metolachlor at 1,600 and 3,200 g ai/ha, and imazethapyr at 75 and 150 g ai/ha. EPTC, trifluralin, dimethenamid, and S -metolachlor applied PPI resulted in minimal (less than 5%) visual injury and with exception of the low rate of dimethenamid causing a 16% reduction in shoot dry weight and the high rate causing an 8% plant height reduction had no adverse effect on plant height, shoot dry weight, seed moisture content, and yield. Imazethapyr applied PPI caused up to 7% visual injury and reduced plant height, shoot dry weight, and yield 8, 18, and 12% at 75 g/ha and 19, 38, and 27% at 150 g/ ha, respectively. Seed moisture content was also reduced by 0.4% with both rates. Based on these results, otebo bean is not tolerant of imazethapyr applied PPI at rates as low as 75 g/ha, the proposed use rate. EPTC, trifluralin, dimethenamid, and S -metolachlor applied PPI have a 2× rate crop safety margin for use in otebo bean weed management.

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)0.000
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 score0.888
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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

Citations12
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueWeed TechnologySame topicWeed Control and Herbicide ApplicationsFrench-language works237,207