Fluctuation Analysis of Redox Potential to Distinguish Microbial Fe(II) Oxidation
Bibliographic record
Abstract
We developed a novel method for distinguishing abiotic and biological iron oxidation in liquid media using oxidation-reduction (redox) potential time series data. The instrument and processing algorithm were tested by immersing the tip of a Pt electrode with an Ag-AgCl reference electrode into an active iron-oxidizing biofilm in a groundwater discharge zone, as well as in two abiotic systems: a killed sample and a chemical control from the same site. We used detrended fluctuation analysis to characterize average root mean square fluctuation behavior, which was distinct in the live system. The calculated α value scaling exponents determined by detrended fluctuation analysis were significantly different at p < 0.001. This indicates that time series of electrode response data may be used to distinguish live and abiotic chemical reaction pathways. Due to the simplicity, portability, and small size, it may be suitable for characterization of extraterrestrial environments where water has been observed, such as Mars and Europa. Key Words: Oxidation-reduction potential-Detrended fluctuation analysis-Iron-oxidizing bacteria. Astrobiology 16, 846-852.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".