Validating the Scalability of Soft X-ray Spectromicroscopy for Quantitative Soil Ecology and Biogeochemistry Research
Bibliographic record
Abstract
Synchrotron-based soft-X-ray scanning transmission X-ray microscopy (STXM) has the potential to provide nanoscale resolution of the associations among biological and geological materials. However, standard methods for how samples should be prepared, measured, and analyzed to allow the results from these nanoscale imaging and spectroscopic tools to be scaled to field scale biogeochemical results are not well established. We utilized a simple sample preparation technique that allows one to assess detailed mineral, metal, and microbe spectroscopic information at the nano- and microscale in soil colloids. We then evaluated three common approaches to collect and process nano- and micronscale information by STXM and the correspondence of these approaches to millimeter scale soil measurements. Finally, we assessed the reproducibility and spatial autocorrelation of nano- and micronscale protein, Fe(II) and Fe(III) densities in a soil sample. We demonstrate that linear combination fitting of entire spectra provides slightly different Fe(II) mineral densities compared to image resonance difference mapping but that difference mapping results are highly reproducible between among sample replicates. Further, STXM results scale to the mm scale in complex systems with an approximate geospatial range of 3 μm in these samples.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".