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Record W2412053916

Effect of Selected Metal Ions on the Mycelial Growth of Sclerotinia Sclerotiorum Isolated From Soybean Field in Rongai, Kenya

2014· article· en· W2412053916 on OpenAlexvenueno aff
Erastus S.K. Mwangi, Erastus Gatebe, Mary Ndung’u

Bibliographic record

VenueInternational Journal of Chemistry · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsSclerotinia sclerotiorumMyceliumChemistryMetal ions in aqueous solutionMetalZincOxalic acidOxalateNuclear chemistryHorticulturePathogenic fungusFood scienceBotanyBiologyBiochemistryInorganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

White mold caused by Sclerotinia sclerotiorum attacks a wide host range of broad-leafed plants which includes soybeans. The effect of twelve metal ions (Hg+2, Co+3, Ag+1, La+3, Cd+2, Cr+3, Cu+2, Zn+2, Mo+5, Sr+2, Sn+4 and Ba+2) on the growth of pathogenic fungus S. sclerotiorum was studied. The fungus was isolated from infected soybean plant collected at Rongai, Kenya. The isolate was tested for the tolerance to metal ions at concentrations of 50.0, 100.0, 250.0 and 500.0 ppm amended into the C: N (35:1) glucose peptone prepared using 1.5% (3.75g) agar culture medium. All the investigated metal ions exhibited concentration dependent mycelial growth using disc diffusion test. Of 12 metal cations tested, only copper and zinc stimulated mycelial growth of S. sclerotiorum mycelial in relatively higher concentrations. Higher concentrations of Hg+2, Ag+1, La+3 and Cd+2 inhibited growth of fungi causing an opaque halo in the medium. FT-IR spectral analysis of culture filtrate reviewed oxalic acid secreted precipitated primarily as oxalate at the periphery of the fungal colony. This work suggests that strong pollution of soil by some heavy metals could be a restrictive factor of development and pathogenicty of S. sclerotiorum fungi in the environment.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.006
GPT teacher head0.199
Teacher spread0.193 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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