Mean Dosage Stimulation Range of Allelochemicals from Crude Extracts of Cucumis africanus Fruit for Improving Growth of Tomato Plant and Suppressing Meloidogyne incognita Numbers
Bibliographic record
Abstract
Successful utilisation of allelochemicals in management of plant-parasitic nematodes depends on their degree of phytotoxicity. Conventional methods of determining phytotoxicity are tedious, with inconsistent results. Plants respond to increased dosages of allelochemicals in a density-dependent growth pattern, which allows the use of the Curve-fitting Allelochemical Response Data computer-based model to determine the mean dosage stimulation range of used allelochemicals. The CARD modelling was used to determine the stimulation range of fermented dried crude extracts of wild cucumber (Cucumis africanus) fruit for improving growth of tomato (Solanum lycopersicon) plants, each infested with 1500 eggs and juveniles of the southern root-knot (Meloidogyne incognita) nematode. Dilutions at 0, 2, 4, 8, 16, 32 and 64% were applied weekly through irrigation system. At 56 days after treatment, CARD demonstrated density-dependent growth patterns as dosages increased. The mean dosage stimulation range of diluted fermented crude extracts, computed from CARD biological indices, was 2.64% dilution for tomato plant. Since at 2% dilution, the material reduced final nematode population density of M. incognita by 90%. The 2.64% was suitable for stimulation of tomato plant and suppression of nematode numbers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".