Effect of sunlight on the infectivity of<i>Cryptosporidium parvum</i>in seawater
Bibliographic record
Abstract
The prevalence of pathogenic microorganisms in seawater can result in waterborne and food borne outbreaks. This study was performed to determine the effect of sunlight and salinity on the die-off of Cryptosporidium parvum. Cryptosporidium parvum oocysts, Escherichia coli, and MS2 coliphage were seeded into tap water and seawater samples and then exposed to sunlight. The die-off of C. parvum in seawater, as measured by infectivity, was greater under sunlight (-3.08 log10) than under dark conditions (-1.31 log10). While, no significant difference was recorded in the die-off of C. parvum, under dark conditions, in tap water as compared to seawater (P < 0.05), indicating that the synergistic effect of salinity and sunlight was responsible for the enhanced die-off in seawater. The die-off of MS2 coliphage and E. coli was greater than that observed for C. parvum under all tested conditions. This indicates that these microorganisms cannot serve as indicators for the presence of C. parvum oocysts in seawaters. The results of the study suggest that C. parvum can persist as infectious oocysts for a long time in seawater and can thus pose a serious hazard by direct and indirect contact with humans.
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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".