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Record W4395455747 · doi:10.17311/tas.2024.69.82

Plant Parasitic Nematodes Associated with Three African Indigenous Vegetables in Southwest Nigeria

2024· article· en· W4395455747 on OpenAlexfundno aff
Leonard Uzoma Amulu, Durodoluwa Joseph Oyedele, O. K. Adekunle

Bibliographic record

VenueTrends in Agricultural Sciences · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsIndigenousGeographyAgroforestryBiologyEcology

Abstract

fetched live from OpenAlex

Background and Objective: African indigenous vegetables are rich in nutrients and medicinal properties that are important for health and vitality, but their availability is on the decline partly due to attacks by plant parasitic nematodes, hence field surveys were conducted in 2016 and 2017 to investigate the distribution of plant parasitic nematodes and their interactions with free-living nematodes in fields planted to three African indigenous vegetables in Southwest Nigeria.Materials and Methods: A total of 180 soil samples were taken from 180 farms in all Local Government Areas (LGAs) visited in the four States in Southwest Nigeria.Samples were taken from three vegetable fields in all the LGAs visited.Nematodes were extracted from 200 mL sub-samples; the nematodes were counted and identified under a compound microscope using a pictorial guide.Results: The results showed that 16 genera of plant parasitic nematodes were found associated with Amaranthus cruentus, Solanum macrocarpon and Telfairia occidentalis.Meloidogyne, Helicotylenchus, Rotylenchulus, Xiphinema, Pratylenchus and Hoplolaimus were the most abundant nematode species encountered in vegetable fields in the study areas.The correlation analysis shows an antagonistic relationship between free-living nematodes and plant parasitic nematodes.Conclusion: There is a need to cultivate vegetable crops to suppress the populations of plant parasitic nematodes in Southwest Nigeria.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.030
GPT teacher head0.240
Teacher spread0.210 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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