Plant Parasitic Nematodes Associated with Three African Indigenous Vegetables in Southwest Nigeria
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".