Bibliographic record
Abstract
The aim of the current study was to investigate the viable plate count, coliform and fecal coliform bacteria on fresh fruits washed with water, and utilization of antimicrobial agents (H2O2, ethanol and citric acid).Pour plate count method was employed for the total plate count (TPC), while multiple tube fermentation techniques were used for coliform bacteria determination.Among all the fruit samples washed with water, the highest value of TPC was observed in apples (15000 CFU/g) and the lowest in pears (2000 CFU/g).The presence of Coliform and fecal Coliform bacteria were found <3 in persimmons, grapes and pears washed with water and treated with H2O2, ethanol and citric acid.The findings concluded that the bacterial contamination in indigenous wholesale places was incredibly the highest, and hydrogen per oxide could be utilized as a washing source to get the better quality of fresh fruits.The increase in health awareness has led to consuming proper wash -down, or specifically proper treatments of fruits to reduce the bacterial contamination and health problems.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.909 | 0.888 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".