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Record W1981791785 · doi:10.1614/wt-03-156r1

Sweet Corn Cultivar Sensitivity to Bentazon

2004· article· en· W1981791785 on OpenAlexaffabout
Shane Diebold, Darren E. Robinson, J.W. Zandstra, John O’Sullivan, Peter H. Sikkema

Bibliographic record

VenueWeed Technology · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBentazonCultivarWeed scienceAgronomyHorticultureBiologyWeed control

Abstract

fetched live from OpenAlex

Five sweet corn cultivars were evaluated for tolerance to bentazon in five field experiments conducted during 2 yr in Ontario. Bentazon was applied postemergence (POST) at 1.08 and 2.16 kg ai/ha, the highest registered rate and twice the highest registered rate, respectively, used in sweet corn in Ontario. When bentazon was applied POST at 1.08 and 2.16 kg/ha to sweet corn cultivar ‘DelMonte 2038’, injury included plant stunting and leaf damage ranging from 6 to 69% and 15 to 90%, respectively. Plant height was reduced to 48 and 100% of the untreated check when treated with bentazon at 1.08 and 2.16 kg/ha, respectively. The visual injury and height reductions were reflected in the marketable yields, which were reduced to 94% when treated with bentazon. Significant reductions in height and marketable yield were not observed in the other four cultivars tested. No correlation was observed between bentazon sensitivity and endosperm genotype. Based on visual injury ratings, sweet corn height, and marketable yield, it was concluded that ‘Calico Belle’, ‘GH 2684’, ‘Reveille’, and ‘Rival’ are tolerant to POST application of bentazon.

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 score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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

Citations17
Published2004
Admission routes2
Has abstractyes

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