Control of Postharvest Decay on Cashew Fruit (<i>Anacardium Occidentale</i> L.) with Aqueous Extract of Cashew Leaf
Bibliographic record
Abstract
The study investigated control of postharvest decay on cashew fruit (Anacardium occidentale L.) with aqueous leaf extract of cashew. Freshly emergent cashew leaves were air-dried for 5 days and then pulverized. Varying concentrations (0.5%, 1%, 1.5%, 2% and 2.5%) of the pulverized cashew leaves were prepared with appropriate volumes of water. Fifteen cashew fruits were dipped separately into each concentration of the prepared extract for 1 minute while untreated cashew fruits served as control. Both the treated and control fruits were stored in dessicators at 28±2°C and 45-50% relative humidity and assessed daily for disease incidence. Phytochemical screening of the extract was also carried out. Results obtained on day 1 of storage indicated that both the control and all treated cashew fruits irrespective of treating concentrations had no disease incidence (0%) which implied that all the cashew fruits were disease free. However, as storage duration progressed, there was a decline in the efficacy of the cashew leaf extract. Only fruits treated with 2% cashew leaf extract maintained 0% disease incidence on day 4 of storage and recorded 30% disease incidence on day 7 of storage when compared with control and other treated fruits that had 100% disease incidence. The antifungal activity of the leaf extract could be traced to different phytochemicals present in it.
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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".