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Record W2038296442 · doi:10.5539/jas.v2n1p72

Influence of Cowpea Mottle Virus and Cucumber Mosaic Virus on the Growth and Yield of Six Lines of Soybean (Glycine Max L.)

2010· article· en· W2038296442 on OpenAlexvenueno aff
Olawale Arogundade, O. S. Balogun, Olumide Shokalu, Taiye Hussein Aliyu

Bibliographic record

VenueJournal of Agricultural Science · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologySowingCucumber mosaic virusInoculationCropAgronomyHorticultureVirusMottlePlant virusVirology

Abstract

fetched live from OpenAlex

Cowpea mottle virus is of localised importance on cowpea in Nigeria infecting several leguminous species andCucumber mosaic virus is of worldwide distribution infecting several horticultural crops.A study was carried out in the screen house of the crop production pavilion, Faculty of Agriculture, University of Ilorin,Ilorin, Kwara State, Nigeria to determine the pathogenic response of six lines of soybean (TGx 1844-18E, TGx 1448-2E,TGx- 1910-8F, TGx 1019-2EN, TGx 1844-4E and TGx 1876-4E) to single and mixed infection with cowpea mottlevirus (CMeV) and cucumber mosaic virus (CMV).The Lines obtained from National Cereal Research Institute, Badeggiwere used for the experiment.Two viable Seeds were sown per 4litre plastic pots filled with sterilized soil; inoculation of viral isolates was carried outmechanically on the soybean at two weeks after planting.The results revealed that all Soybean lines tested were susceptible to single and mixed infection of the two viruses.However the tolerance level of the legumes, as shown by the percentage loss in selected growth and yield attributesindicated that line TGx 1910-8F is the most tolerant while TGx 1844-18E is the most susceptible to single and mixedinfection.Hence, TGx 1910-8F is most suitable in areas where there is an outbreak of disease caused by CMeV and CMV.

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)0.001
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 score0.800
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.215
Teacher spread0.202 · 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

Citations7
Published2010
Admission routes1
Has abstractyes

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