Composition, Reactivity and Regulation of Extracellular Metal-Reducing Structures (Bacterial Nanowires) Produced by Dissimilatory Metal - Reducing Bacteria
Bibliographic record
Abstract
Approach. Previously, using conventional and cryoTEM techniques, surface physicochemistry assays, NMR structural analysis, etc., we showed that the structure and composition of Shewanella's lipopolysaccharide (LPS) and capsular polysaccharide (PS) significantly determined overall cell surface physicochemistry. In our study a strong correlation between such macroscopic parameters as surface electronegativity, hydrophobicity or hydrophilicity, and bacterial adhesion to hematite was observed. Rough LPS strains exhibited more than an order higher affinity and maximal sorption capacity to hematite when compared to encapsulated strains. These general trends, however, characterize bacterial adhesion only as a bulk process, being unable to reveal finer mechanisms taking place at the level of an individual cell. Cell surface physicochemical and structural heterogeneity suggests much more complex interactions at the bacterial-mineral interface than predicted by such approaches operating within macroscopic parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".