In search of the microbe/mineral interface: quantitative analysis of bacteria on metal surfaces using vertical scanning interferometry
Bibliographic record
Abstract
To understand the development of biofilms on metal surfaces, analysis of initial bacterial attachment to surfaces is crucial. Here we present the results of a study, using Shewanella oneidensis MR-1 as a model organism, in which vertical scanning interferometry (VSI) was used to investigate the initial stages of cell attachment to glass, steel and aluminium surfaces. It was found that while VSI gave unambiguous results with opaque surfaces, when reflective surfaces were used, an artifact sometimes appeared, with the bacteria appearing as rod-shaped pits rather than as cells on the surface. When the bacteria were altered to increase opacity, this artifact disappeared, and upon further investigation, it was found that the observational artifact was the result of a conflict between light reflected from the bacteria and the light reflected from the bacteria-metal interface. These results suggest that not only can bacteria be measured on surfaces using VSI, but with some modifications to the analytical software, there may be a unique window for studying the bacterial/substrate interface that can be used for quantitative observations. Imaging and characterization of the bacteria-substrate interface in vivo (previously invisible) will provide new insights into the interactions that occur at this important juncture.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".