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Record W7036330473

Biology of aphelinus certus and its susceptibility to insecticides used for soybean aphid management

2010· dissertation· en· W7036330473 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2010
Typedissertation
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsnot available
Fundersnot available
KeywordsSoybean aphidAphidIntegrated pest managementBiological pest controlNatural enemiesParasitoid
DOInot available

Abstract

fetched live from OpenAlex

The soybean aphid ('Aphis glycines' Matsumura) is a serious pest of soybean ('Glycine max' L.) in North America. Currently, soybean aphid management has focused on foliar applied insecticides. However, there is a growing body of evidence that natural enemies can regulate soybean aphid populations. Because soybean aphid has a high intrinsic rate of increase, natural enemies alone are not aiways a viable management option for soybean aphid. Integration of natural enemies and other management options will be necessary for reliable soybean aphid management. Determining the compatibility of natural enemies and insecticides is extremely important. In 2006, the exotic soybean aphid parasitoid 'Aphelinus certus' Yanosh was discovered in Ontario. This parasitoid was found to be distributed throughout southern Ontario making it an ideal candidate for incorporation into soybean aphid management decisions. Temperature-dependent developmental parameters were investigated, as well as the susceptibility of this parasitoid to insecticides currently or potentially registered for soybean aphid management in Canada.

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 score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.029
GPT teacher head0.307
Teacher spread0.279 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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