Applications of Scanning Electron Microscopy in Geomicrobiology
Bibliographic record
Abstract
From mineralized biofilms of ancient or “extreme” environments to the nth replicate of laboratory-based biofilm experiments, geomicrobiological samples containing microbes associated with primary minerals or secondary (biogenic) mineral precipitates are highly diverse. The foremost advantage of scanning electron microscopy for geomicrobiology is that it provides high-resolution micrographs of cells and biofilms in association with minerals. These micrographs provide visual evidence of biogeochemical processes, which helps to explain phenomena that occur in the natural environment or in the laboratory. In addition to high-resolution secondary electron or backscatter electron modes of imaging, scanning electron microscopes can be equipped with a range of microanalytical tools, thereby extending the breadth of analytical capacities. Analytical techniques such as energy dispersive spectroscopy and electron backscatter diffraction analysis characterize the chemical composition and crystallography of biofilms and (bio)minerals, respectively, down to the micrometer scale. In addition, a focused ion beam can be used for nanomachining samples to provide a view beneath the outer surface of a sample or can be used as a technique for preparing samples for transmission electron microscopy. To provide the reader with an overview of tools and techniques, this chapter will explain a number of widely used preparation techniques, including whole-mounts, petrographic thin sections, polished blocks, and focused ion beam milling. By using these techniques, various types of geomicrobiological materials will be examined and used to guide and develop the necessary skills for interpreting biogeochemical processes from structural and chemical information obtained through secondary electron and backscatter electron micrographs and associated microanalyses.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".