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Record W2175264175 · doi:10.1080/07060661.2015.1105873

Tan spot of lima bean caused by <i>Boeremia exigua</i> var. <i>exigua</i> in New York State, USA

2015· article· he· W2175264175 on OpenAlexvenueno aff
Adrienne Gorny, Julie R. Kikkert, Amara R. Dunn, H. R. Dillard, Christine D. Smart, Sarah J. Pethybridge

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2015
Typearticle
Languagehe
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsPhaseolusExiguaBiologyLeaf spotCultivarFungusInoculationBotanySpodopteraHorticultureGeneGenetics

Abstract

fetched live from OpenAlex

In 2013, a new foliar disease was observed in processing lima bean (Phaseolus lunatus L.) plants in fields across western New York State, USA. The fungus Boeremia exigua var. exigua (Desm.) Aveskamp, Gruyter & Verkley was consistently isolated from lesions. The fungus was identified by characterization of morphological features and multiple genes (internal transcribed spacer, partial actin, β-tubulin, translation elongation factor 1-α and calmodulin) informative for speciation within the Didymellaceae. Pathogenicity was confirmed by inoculation of three lima bean cultivars in a repeated greenhouse experiment. Snap bean (Phaseolus vulgaris L.) and soybean (Glycine max (L.) Merr.), which are commonly encountered in the cropping rotation in New York State, were also inoculated, and demonstrated susceptibility to the pathogen. To the best of our knowledge, this is the first report of Boeremia exigua var. exigua causing tan spot disease on foliage of lima bean in New York State, USA.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.209

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 communication0.0000.000
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 head0.021
GPT teacher head0.217
Teacher spread0.196 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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