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Record W2148137815 · doi:10.1614/wt-03-140r

Snap Bean Tolerance to Herbicides in Ontario

2004· article· en· W2148137815 on OpenAlexaffabout
Kristen E. McNaughton, Peter H. Sikkema, Darren E. Robinson

Bibliographic record

VenueWeed Technology · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsClomazoneMetolachlorSnapAgronomyYield (engineering)HorticultureCropWeed controlBiologyPesticidePhysicsAtrazine

Abstract

fetched live from OpenAlex

Snap bean was evaluated for sensitivity to a number of herbicides in field studies conducted during a 2-yr period in Exeter, ON. Preemergence (PRE) applications of metolachlor (1,600 and 3,200 g ai/ha), imazethapyr (75 and 150 g ai/ha), and clomazone plus metobromuron (840 + 1,000 g ai/ha and 1,680 + 2,000 g/ha) were evaluated for visual injury at 7, 14, and 28 d after emergence. Postemergence (POST) applications of imazamox plus fomesafen (25 + 200 g ai/ha and 50 + 400 g/ha), quizalofop-P (72 and 144 g ai/ha), and clethodim (90 and 180 g ai/ha) also were evaluated for visual injury 7, 14, and 28 d after treatment. Plant height and crop yield were assessed for all treatments. Visual injury, stunting, and yield loss were not observed in the metolachlor treatments. Imazethapyr (150 g/ha) caused stunting and reduced snap bean yield in both study years. Clomazone plus metobromuron (1,680 + 2,000 g/ha) injured and stunted snap bean in both years of the study and reduced yield in 2000. Imazamox plus fomesafen (50 + 400 g/ha) injured snap bean in both years but only reduced yield in 2000. Quizalofop-P injured snap bean but did not reduce plant height or yield. Clethodim did not injure, stunt, or reduce yield of snap bean. Metolachlor (PRE), imazamox plus fomesafen (POST), quizalofop-P (POST), and clethodim (POST) have excellent potential as weed management tools in snap bean in Ontario.

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 score0.910
Threshold uncertainty score0.768

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 head0.011
GPT teacher head0.206
Teacher spread0.194 · 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

Citations2
Published2004
Admission routes2
Has abstractyes

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