Spatial Distributions of <i>Cryptosporidium</i> Oocysts in Porous Media: Evidence for Dual Mode Deposition
Bibliographic record
Abstract
Spatial distributions of Cryptosporidium parvum oocysts in columns packed with uniform glass-bead collectors were measured over a broad range of physicochemical conditions. Oocyst deposition behavior is shown to deviate from predictions based on classical colloid filtration theory (CFT) in the presence of repulsive (unfavorable) colloidal interactions. Specifically, CFT tends to predict greater removal of oocysts (less transport) than that observed in controlled laboratory experiments. Comparison of oocyst retention with results obtained using polystyrene latex particles of similar size suggests that mechanisms controlling particle deposition are the same in both systems. At a given ionic strength, the deposition of Cryptosporidium oocysts is generally greater than that of the microspheres; however, this discrepancy is partly attributable to large differences in oocyst and microsphere zeta potentials. A dual deposition mode (DDM) model is applied which considers the combined influence of "fast" and "slow" oocyst deposition due to the concurrent existence of favorable and unfavorable oocyst-collector interactions. Model simulations of retained oocyst profiles and suspended oocyst concentration at the column effluent are consistent with experimental data. Because classic CFT does not account for the effect of dual mode deposition (i.e., simultaneous "fast" and "slow" oocyst deposition), these observations have important implications for predictions of oocyst transport in subsurface environments, where repulsive electrostatic interactions predominate. Supporting elution experiments further suggest that specific surface interactions between oocyst wall macromolecules and the glass bead collectors could retard or even completely inhibit oocyst release upon perturbation in solution chemistry.
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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) | 0.000 | 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".