Microbial response to environmental gradients in a ceramic‐based diffusion system
Bibliographic record
Abstract
A solid, porous matrix was used to establish steady-state concentration profiles upon which microbial responses to concentration gradients of nutrients or antimicrobial agents could be quantified. This technique relies on the development of spatially defined concentration gradients across a ceramic plate resulting from the diffusion of solutes through the porous ceramic matrix. A two-dimensional, finite-element numerical transport model was used to predict the establishment of concentration profiles, after which concentration profiles of conservative tracers were quantified fluorometrically and chemically at the solid-liquid interface to verify the simulated profiles. Microbial growth responses to nutrient, hypochloride, and antimicrobial concentration gradients were then quantified using epifluorescent or scanning confocal laser microscopy. The observed microbial response verified the establishment and maintenance of stable concentration gradients along the solid-liquid interface. These results indicate the ceramic diffusion system has potential for the isolation of heterogeneous microbial communities as well as for testing the efficacy of antimicrobial agents. In addition, the durability of the solid matrix allowed long-term investigations, making this approach preferable to conventional gel-stabilized systems that are impeded by erosion as well as expansion or shrinkage of the gel.
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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".