Investigation of Parasitic Contaminations of Vegetables Sold in Markets in the City of Tabriz in 2014
Bibliographic record
Abstract
Parasitic diseases are among the most common problems in developing countries. Various parasitic forms such as cysts, larvae and eggs of the parasite are transmitted orally through the consumption of vegetables. So, the aim of this study was to evaluate parasitic contamination of fresh vegetables consumed in the city of Tabriz. This cross-sectional study was conducted for 3 months in 2014 in Tabriz. In this study, 1620 samples from 10 kinds of vegetables (leeks, basil, mint, chives, radishes, parsley, lettuce, watercress, tarragon and coriander) were collected from 54 vegetable shops. After the washing process, centrifugation, sediment preparation and staining, each sample was examined for parasitic contamination. The parasitic infection was observed in 316 (19.5%) which 141 samples (8.7%) was related to metazoan and 175 samples (10.8%) was related to protozoan. the highest contamination was observed in leeks and the lowest in radish. Most parasitic contaminations were related to Ascaris eggs and E. coli cyst and the lowest parasitic contaminations were related to Heterophyes eggs. It can be concluded that vegetables sold in Tabriz are potential sources of human contamination with parasites. It is suggested that the use of fertilizers of human source in agriculture should be avoided.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".