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Record W2171066848 · doi:10.4039/n06-095

Toward management guidelines for the soybean aphid in Quebec. I. Feeding damage in relationship to seasonality of infestation and incidence of native predators

2007· article· en· W2171066848 on OpenAlexaffabout
Marc Rhainds, Michèle Roy, Gaétan Daigle, Jacques Brodeur

Bibliographic record

VenueThe Canadian Entomologist · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsUniversité LavalUniversité de Montréal
FundersAnimal and Plant Health Inspection Service
KeywordsInfestationBiologyAphididaeSoybean aphidAphidPredationAgronomyPhenologyHomopteraPEST analysisHemipteraPopulationHorticultureBotanyEcology

Abstract

fetched live from OpenAlex

Abstract A study was conducted in 2004 and 2005 to test the hypotheses that the severity of damage caused by the soybean aphid, Aphis glycines (Hemiptera: Aphididae), is minimized by the activity of predators and declines with the maturity of soybeans, Glycine max (L.) Merr. (Fabaceae), at the time of infestation. In caged subplots where predators were excluded, aphids attained a high density following experimental infestation of soybeans, resulting in severe reductions of yield, particularly when plants were infested early in the season. A guild of generalist predators consisting predominantly of ladybird beetles colonized plants in uncaged subplots, resulting in a low rate of population growth following infestation of soybeans with aphids and a relatively weak impact on the soybean yield. The soybean yield declined as the density of aphids (number per plant), and the maturity of soybeans at the time of infestation, increased. Our results suggest that A. glycines represents an occasional pest of soybean in Quebec, because of ( i ) temporal asynchrony between the late-season infestation by aphids and the most susceptible phenological stage of soybeans (vegetative or flowering) and ( ii ) biological control by natural enemies.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.073
GPT teacher head0.313
Teacher spread0.239 · 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 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

Citations34
Published2007
Admission routes2
Has abstractyes

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