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Record W2298892794 · doi:10.1139/cjps-2015-0258

Factors affecting mycelium pigmentation and pathogenicity of <i>Sclerotinia sclerotiorum</i> on Valencia peanut

2016· article· en· W2298892794 on OpenAlexvenueno aff
Phillip Lujan, Soum Sanogo, Naveen Puppala, Jennifer J. Randall

Bibliographic record

VenueCanadian Journal of Plant Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsSclerotinia sclerotiorumMyceliumBiologyOxalic acidBotanyAscocarpHorticulture

Abstract

fetched live from OpenAlex

Sclerotinia sclerotiorum infects a broad range of plant hosts, and is typically identified by the production of white mycelium and black sclerotia on infected plants and culture. Isolates of S. sclerotiorum with darkly-pigmented mycelium have been reported on Valencia peanut in New Mexico and Texas. This study was conducted to determine the relationship between dark pigmentation in mycelium, oxalic acid production, and pathogenicity of S. sclerotiorum on Valencia peanut. A darkly-pigmented (SD) and a mutant, non-pigmented (SW) isolate of S. sclerotiorum were compared for vegetative growth on various growth media under different environmental conditions, and for pathogenicity on peanut. The SD isolate became darker in mycelium pigmentation as temperature increased beyond 20 °C and at pH between 5.0 and 6.0. Pigmentation in the SW isolate remained unaffected. Oxalic acid production did not increase in either SD or SW isolates when grown on media amended with pure oxalic acid or its precursor arabinose. The SD isolate caused peanut plant death and necrotic lesions on leaflets, while the SW isolate did not cause any symptoms. The differential behavior of SD and SW isolates in pathogenicity does not appear to be associated with mycelium pigmentation.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.600

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 head0.029
GPT teacher head0.195
Teacher spread0.166 · 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

Citations17
Published2016
Admission routes1
Has abstractyes

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