FECAL CONTAMINATION OF ICE CREAM MIX POWDER SOLD IN ASSIUT CITY
Bibliographic record
Abstract
One hundred and fifty random samples of ice cream mix powder with chocolate, mango, strawberry, vanilla, orange, banana and Nescafe flavors (200 grams weigh each) were collected from different groceries and supermarkets in Assiut City over a period of one year and examined for fecal contamination. Our current results showed that, Enterobacteriaceae were isolated from 38.7% of samples with a minimum of 102, a maximum of 8.7X102 and an average of 3.2X10/g. The highest frequency distribution 46 (79.32%) had numbers of less than 102 cfu/g and 12 (20.68%) had counts ranged from 102-<103 cfu/g. Enterococci could be isolated from 10.7% of the examined samples with a minimum, a maximum and an average count of <100, 6X103 and 4.08X103/g, respectively. The frequency distribution of positive samples was descending from 9 (56.25%), 4 (25.0%) and 3 (18.75%) containing Enterococci in counts ranged from 102-<103, <102 and 103-<104/g, respectively. Otherwise, 5 (3.33%) samples were contaminated by both coliforms and fecal coliforms with frequency distribution of 3 (60%) that had counts of less than 10/g and 2 (40%) had counts ranged from 10-<102/g. E. coli could not be detected. Recommendations were suggested to control the presence of such microorganisms in ice cream mix powder samples to avoid their undesirable changes that resulted in economic losses as well as public health hazard.
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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 | 0.001 | 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".