Transport of <i>Cryptosporidium</i> Oocysts in Porous Media: Role of Straining and Physicochemical Filtration
Bibliographic record
Abstract
The transport and filtration behavior of Cryptosporidium parvum oocysts in columns packed with quartz sand was systematically examined under repulsive electrostatic conditions. An increase in solution ionic strength resulted in greater oocyst deposition rates despite theoretical predictions of a significant electrostatic energy barrier to deposition. Relatively high deposition rates obtained with both oocysts and polystyrene latex particles of comparable size at low ionic strength (1 mM) suggest that a physical mechanism may play a key role in oocyst removal. Supporting experiments conducted with latex particles of varying sizes, under very low ionic strength conditions where physicochemical filtration is negligible, clearly indicated that physical straining is an important capture mechanism. The results of this study indicate that irregularity of sand grain shape (verified by SEM imaging) contributes considerably to the straining potential of the porous medium. Hence, both straining and physicochemical filtration are expected to control the removal of C. parvum oocysts in settings typical of riverbank filtration, soil infiltration, and slow sand filtration. Because classic colloid filtration theory does not account for removal by straining, these observations have important implications with respect to predictions of oocyst transport.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".