Linking Spectral Induced Polarization (SIP) and Subsurface Microbial Processes: Results from Sand Column Incubation Experiments
Bibliographic record
Abstract
Geophysical techniques, such as spectral induced polarization (SIP), offer potentially powerful approaches for in situ monitoring of subsurface biogeochemistry. The successful implementation of these techniques as monitoring tools for reactive transport phenomena, however, requires the deconvolution of multiple contributions to measured signals. Here, we present SIP spectra and complementary biogeochemical data obtained in saturated columns packed with alternating layers of ferrihydrite-coated and pure quartz sand, and inoculated with Shewanella oneidensis supplemented with lactate and nitrate. A biomass-explicit diffusion-reaction model is fitted to the experimental biogeochemical data. Overall, the results highlight that (1) the temporal response of the measured imaginary conductivity peaks parallels the microbial growth and decay dynamics in the columns, and (2) SIP is sensitive to changes in microbial abundance and cell surface charging properties, even at relatively low cell densities (<10 8 cells mL –1 ). Relaxation times (τ) derived using the Cole–Cole model vary with the dominant electron accepting process, nitrate or ferric iron reduction. The observed range of τ values, 0.012–0.107 s, yields effective polarization diameters in the range 1–3 μm, that is, 2 orders of magnitude smaller than the smallest quartz grains in the columns, suggesting that polarization of the bacterial cells controls the observed chargeability and relaxation dynamics in the experiments.
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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 | 0.001 |
| 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".