Interpreting Deposition Patterns of Microbial Particles in Laboratory-Scale Column Experiments
Bibliographic record
Abstract
The transport and fate of microbial particles in subsurface environments is controlled by their capture (natural filtration) by sediment grains. Typically, filtration models used to describe microbe removal in porous media predict exponential decrease in microbial particle concentration with travel distance. However, a growing body of laboratory-scale column experiments suggests that the retained microbial particle profiles decay nonexponentially. The observed behavior may be attributed to the heterogeneity in the interactions between microbial particles and sediment grains, most likely due to the inherent variability in the microbial particles. This factor can be incorporated into classical colloid filtration (deposition) theory by inclusion of a distribution in the deposition rate coefficient. We show that certain distributions of the deposition rate coefficient (i.e., log-normal, bimodal, and power-law distributions) give rise to nonexponential deposition patterns. Comparisons of model predictions to experimental data indicate that the observed nonexponential deposition behavior of bacteria and virus particles may be attributed to a broad range (i.e., a power-law distribution) of microbial deposition rates. Other mechanisms such as particle release and blocking by previously deposited microbial particles are also shown to be potential sources of deviation from the classical filtration theory. Our results further suggest that monitoring fluid-phase particle concentration is insufficient for accurate characterization of the deposition and transport behavior of microbial particles in saturated porous media. Rather, the shape of the microbial particle retention profile is shown to be a key indicator of the mechanisms controlling microbial deposition and 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".