A Phenomenological Model for Particle Retention in Single, Saturated Fractures
Bibliographic record
Abstract
Fractured aquifers are some of the most poorly characterized subsurface environments despite posing one of the highest risks to the protection of potable groundwater. This research was designed to improve the understanding of the factors affecting particle transport through fractures by developing a phenomenological model based on laboratory-scale transport data. The model presented in this research employed data from over 70 particle tracer tests conducted in single, saturated, variable-aperture fractures that were obtained from the natural environment and fractured in the laboratory or cast from epoxy in the laboratory. The particles employed were Escherichia coli RS2-GFP and microspheres. The tracer experiments were conducted in natural (dolomitic limestone and granite) as well as epoxy replicas of the natural fractures. The multiple linear regression analysis revealed that the most important factors influencing particle retention in fractures are the ratio of the ionic strength of solution to collector charge, the ratio of particle to collector charge, and the ratio of advective to diffusive forces as described by the Peclet number. The model was able to reasonably (R(2) = 0.64) predict the fraction of particles retained; however, it is evident that some factors not accounted for in the model also contributed to retention. This research presents a novel approach to understanding particle transport in fractures, and illustrates the relative importance of various factors affecting the transport mechanisms. The utility of this model lies in the increased understanding of particle transport in fractures, which is extremely useful for directing future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".