FeAs<sub>2</sub> biomineralization on encrusted bacteria in hot springs: an ecological role of symbiotic bacteria
Bibliographic record
Abstract
Bacterial FeAs2 mineralization was found in the reddish-brown microbial mats that have grown on the walls of the drainage systems of Masutomi Hot Springs, Yamanashi Prefecture, Japan. The reddish-brown microbial mats, which are mainly composed of bacilliform and coccoid types of bacteria, have been analyzed and observed by microtechniques to interpret the bacterial biomineralization and search for the clues to bioremediation. These bacteria accumulate Fe and As along with other trace elements to form various biominerals. The electron diffraction (ED) pattern of the bacterial capsule identified lollingite (FeAs2) and calcite (CaCO3) on the surface of the cell. Based on Fourier-transform infrared absorbance spectroscopy (FTIR) analysis, the presence of organic components such as CH, C=O, CNH, COOH, and NH in the reddish-brown microbial mats emphasized the metal-binding potential of the bacteria. X-ray diffraction (XRD) data showed the poorly crystalline character of the precipitates, which consist mainly of hydrous iron oxides (2.7 Å (1 Å = 0.1 nm)). The FeAs2 biominerals form by adsorption onto the bacterial cell wall, as demonstrated by microscopic observations and spectroscopic analysis. These showed that bacteria in the reddish-brown microbial mats have the ability to form biominerals with heavy metals and toxic metalloids like As. Particularly significant in hot spring environments is the role of symbiotic and toxic-resistant bacteria, which have the ability to adapt to high As concentrations. Bacterial FeAs2 mineralization might also be considered a mechanism by which toxic As is removed from the aquatic ecosystem. The results provide evidence for detoxification processes and offer clues to possible methods of bioremediation.
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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 | 0.000 |
| 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".