Pore‐scale modeling of competition and cooperation of multispecies biofilms for nutrients in changing environments
Bibliographic record
Abstract
Abstract In this article, we developed a pore‐scale model of integrated lattice Boltzmann method and cellular automata to investigate competitive growth of aerobic nitrite and ammonium oxidizers in a bioreactor. The results showed that inlet nutrient concentrations have significant effects on maximum biofilm concentration, ratio of microorganisms' concentrations, growth pattern, and time. The local availability of oxygen could control the competition, resulting in different growth patterns. The coexistence of ammonium and nitrite in same inlet zone increased not only the biofilm concentration (7%) but also the ratio of microorganisms' concentrations (36%). Although this coexistence decreased the total biofilm concentration in some cases, it increased the growth rate about 25%. Changes of the maximum biomass concentration could change biofilm concentration of about 40% and microorganisms' concentrations ratio of about 30%. This framework provides a powerful tool to improve our understanding of dynamic interdependency of many complex microbial consortia systems with environments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".