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Record W2177208417 · doi:10.1614/wt-03-124r

Management Practices Influencing Herbicide Resistance in Wild Oat

2004· article· en· W2177208417 on OpenAlexaffabout
Hugh J. Beckie, Linda M. Hall, Scott Meers, James J. Laslo, F. Craig Stevenson

Bibliographic record

VenueWeed Technology · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of AlbertaAgriculture Food and Rural DevelopmentAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAgronomyAcetolactate synthaseBiologyHerbicide resistanceTillagePendimethalinCrop rotationResistance (ecology)Perennial plantWeedCrop

Abstract

fetched live from OpenAlex

A 3-yr study was conducted in Wheatland County, Alberta to determine if agronomic practices of growers influenced the occurrence of herbicide resistance in wild oat. Wild oat seeds were collected in 33 fields in 1997 and in 31 fields in each of 1998 and 1999 (one field per grower). Seedlings were screened for resistance to two acetyl-CoA carboxylase (ACCase) inhibitors, imazamethabenz, an acetolactate synthase (ALS) inhibitor, and triallate, a thiocarbamate herbicide. A questionnaire on herbicide resistance awareness and management practices was completed by each grower. Both ACCase and ALS inhibitor resistance in wild oat were linked to a lack of crop rotation diversity. In addition, ALS inhibitor–resistant wild oat was associated with conservation-tillage systems and recent use of herbicides with that mode of action. Results of this study suggest that timely tillage and inclusion of fall-seeded and perennial forage crops in rotations will effectively slow the selection of resistance in this grass species.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.567

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.012
GPT teacher head0.234
Teacher spread0.223 · 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 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

Citations45
Published2004
Admission routes2
Has abstractyes

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