Bibliographic record
Abstract
Fungal contamination in fruits and vegetables represents a significant challenge to food safety, economic stability, and public health. The present study investigates the prevalence and diversity of fungi in rotten fruits and vegetables viz., Garlic (Allium sativum), Onion (Allium cepa), Cauliflower (Brassica oleracea), Papaya (Carica papaya), Tomato (Solanum lycopersicum), Ridge gourd (Luffa acutangula), Capsicum (Capsicum annuum), Beans (Phaseolus), Turnip (Brassica oleracea gongylodes), Carrot (Daucus carota), Ginger (Zingiber officinale), Potato (Solanum tuberosum), Chilli (Capsicum frutescens), Sapota (Manilkara zapota), Cucumber (Cucumis sativus). A comprehensive analysis was conducted using samples collected from local markets. Samples were prepared by ten serial dilutions and inoculated on potato dextrose agar media and incubated at room temperature for 4-5 days. Through microscopic examination using lactophenol cotton blue method and colony characteristics, the predominant fungal species were identified. Aspergillus flavus, Aspergillus fumigatus, Aspergillus niger, Aspergillus parasiticus, Corynespora sp., Alternaria sp., Fusarium sp., Mucor sp., Rhizopus microsporus and Rhizopus stolonifer were among the most frequently isolated genera from the samples. The findings highlight the critical need for improved handling and storage protocols to mitigate fungal contamination and its associated risks. The present study provides valuable insights into the fungal ecology of decaying fruits and vegetables, contributing to the development of effective strategies for managing post-harvest fungal contamination.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".