The Effect of Matrix Properties and Preferential Pathways on the Transport of<i>Escherichia coli</i>RS2-GFP in Single, Saturated, Variable-Aperture Fractures
Bibliographic record
Abstract
Fractured aquifers are a relatively under-studied area of groundwater science particularly because of the heterogeneities present in fractures which make it difficult to understand and predict the transport and retention of contaminants. This research was designed to elucidate some of the factors that contribute to particle transport and retention in fractures using solute and particle tracers in a natural rock fracture and a transparent epoxy replica of that same fracture. Significantly less attachment was observed from the tracer experiments conducted in the replica fracture illustrating the large effect that matrix properties have on transport and retention of particles in fractures. The E. coli RS2-GFP tracer experiments conducted in the replica fracture show that increasing specific discharge results in increasing recovery; however, there is a critical specific discharge at which particle recovery seems to steady or slightly decrease. Images were collected of the E. coli RS2-GFP transport through the epoxy replica fracture, which capture for the first time the preferential pathways of E. coli in fractures, and also demonstrate a slight broadening of the dominant preferential pathway under increasing flow conditions. These results are instructive to the development and improvement of predictive models for particle transport in fractured aquifers.
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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 | 0.001 |
| 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 | 0.000 | 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".