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Record W3104373223 · doi:10.22543/0090-0222.2361

Karshomyia caulicola (Diptera: Cecidomyiidae) Associated with Sclerotinia-Infected Soybean in the United States and Canada

2020· article· en· W3104373223 on OpenAlexaffabout
Robert L. Koch, Bruce D. Potter, Joseph Moisan-De Serres, Janet J. Knodel, Veronica Calles Torrez, John Gavloski, Theresa M. Cira, Mads Bartz, Raymond J. Gagné

Bibliographic record

VenueThe Great Lakes Entomologist · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHymenoptera taxonomy and phylogeny
Canadian institutionsAgriculture Food and Rural DevelopmentMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
FundersMinnesota Soybean Research and Promotion CouncilNorth Dakota Soybean CouncilNorth Central Soybean Research Program
KeywordsBiologyCecidomyiidaeMidgePEST analysisGallSclerotinia sclerotiorumPopulationBotanySclerotiniaAgronomy

Abstract

fetched live from OpenAlex

The white-mold gall midge, Karshomyia caulicola Coquillett, was documented in association with soybean, Glycine max (L.) Merr., infected with the fungus Sclerotinia sclerotiorum (Lib.) de Bary. This mycetophagous cecidomyiid appears widespread in the northern soybean producing region, with confirmed detections from Minnesota, North Dakota and Québec. Though likely not a pest of soybean plants, the presence of K. caulicola in soybean fields may complicate identification, population assessment and decision making for soybean gall midge, Resseliella maxima Gagné, which is a recently described pest of soybean. Here, we provide an overview of the known biology and distribution of K. caulicola and descriptions to aid in distinguishing these two cecidomyiids.

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 score0.044
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.196
Teacher spread0.169 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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