Modelling biofilm‐induced formation damage and biocide treatment in subsurface geosystems
Bibliographic record
Abstract
Biofilm growth in subsurface porous media, and its treatment with biocides (antimicrobial agents), involves a complex interaction of biogeochemical processes which provide non-trivial mathematical modelling challenges. Although there are literature reports of mathematical models to evaluate biofilm tolerance to biocides, none of these models have investigated biocide treatment of biofilms growing in interconnected porous media with flow. In this paper, we present a numerical investigation using a pore network model of biofilm growth, formation damage and biocide treatment. The model includes three phases (aqueous, adsorbed biofilm, and solid matrix), a single growth-limiting nutrient and a single biocide dissolved in the water. Biofilm is assumed to contain a single species of microbe, in which each cell can be a viable persister, a viable non-persister, or non-viable (dead). Persisters describe small subpopulation of cells which are tolerant to biocide treatment. Biofilm tolerance to biocide treatment is regulated by persister cells and includes 'innate' and 'biocide-induced' factors. Simulations demonstrate that biofilm tolerance to biocides can increase with biofilm maturity, and that biocide treatment alone does not reverse biofilm-induced formation damage. Also, a successful application of biological permeability conformance treatment involving geologic layers with flow communication is more complicated than simply engineering the attachment of biofilm-forming cells at desired sites.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| 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".