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Record W2027093174 · doi:10.1614/ws-07-172.1

Herbicide Rate Effects on Weed and Root Maggot Dynamics in Canola

2008· article· en· W2027093174 on OpenAlexafffundabout
Jim S. Broatch, Lloyd M. Dosdall, John T. O’Donovan, K. Neil Harker, George W. Clayton

Bibliographic record

VenueWeed Science · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Alberta
FundersAlberta Canola Producers Commission
KeywordsCanolaWeedBiologyAnthomyiidaeAgronomyWeed controlInfestationMaggotCropBiological pest controlHorticultureBotanyPEST analysis

Abstract

fetched live from OpenAlex

Weed management strategies can influence insect infestations in field crops, yet no attempts have been made previously to manipulate weed populations in canola for integrated weed and insect management. Field studies were conducted during 2003 to 2005 at Lacombe and Beaverlodge, Alberta, Canada to manipulate weed and root maggot, Delia spp. (Diptera: Anthomyiidae), interactions in canola. Densities of monocot weeds were varied by altering herbicide applications, with rates ranging from 0 to 100% of the rate recommended. Weed populations declined, and yields were variable with increased herbicide rates. Root maggot damage decreased with increases in monocot weed dry weight for both canola species at both study sites. Results support the hypothesis that heterogenous environments, arising from mixed populations of monocot weeds with canola, minimize opportunities for females of Delia spp. to complete the behavioral sequence required for oviposition, leading to reduced infestation levels in weedy systems. However, effects of dicot weeds on root maggot infestations varied between sites as a result of site-related differences in weed species complexes. When wild mustard was common, crop damage increased, because this weed can serve as an alternate host for root maggots. The study emphasizes the importance of adopting crop management practices that are compatible for both weed and root maggot control.

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 score0.972
Threshold uncertainty score0.995

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 head0.008
GPT teacher head0.202
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

Citations7
Published2008
Admission routes3
Has abstractyes

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