Dependence of<i>In Situ</i>Bacterial Fe(II)-Oxidation and Fe(III)-Precipitation on Sequential Reactive Transport
Bibliographic record
Abstract
A study was conducted to determine in situ rates of Fe(II) oxidation and Fe(III) precipitation along a 5.0 m reach of a ferruginous groundwater discharge zone under two distinct conditions; (i) the natural state featuring abundant flocculent mats of bacteriogenic iron oxides (BIOS) produced by Fe(II)-oxidizing bacteria, and (ii) after a manual washout of the streambed to remove the microbial mat. Examination of mat samples by differential interference contrast light microscopy revealed tangled meshworks of filamentous Leptothrix sheaths and helical Gallionella stalks intermixed with fine-grained hydrous ferric oxide (HFO) precipitates. The greatest accumulation of BIOS mat was 1.0 m downstream of the groundwater spring. Redox potential (Eh) increased sharply from 200 mV to over 300 mV over the last 2.0 m of the reach. Similarly, dissolved oxygen increased from < 10% saturation to almost 100% saturation over the last 2.0 m of the reach, whereas pH increased from 6.4 to 7.3. Pseudo-first-order rate constants determined on the basis of analytical solutions to sequential partial differential advection-dispersion-reaction equations for the linear Fe(II)→Fe(III)→HFO reaction network yielded in situ Fe(II) oxidation rate constants (kox) of 1.70 ± 0.20 min−1 in natural conditions and 0.48 ± 0.14 min−1 after washout. Corresponding Fe(III)-precipitation rates (kp) before and after washout were 3.45 ± 0.10 min−1 and 0.90 ± 0.01 min−1, respectively. These values for kox and kp are higher than estimates obtained from closed batch microcosm and laboratory experiments, underscoring the crucial dependence of in situ Fe(II) oxidation and Fe(III) precipitation rates on advective and dispersive mass transport. The results also highlight the influence that BIOS microbial mats exert on the reaction kinetics of the multiple heterogeneous reactions contributing not only to Fe(II)/Fe(III) redox transformations in groundwater discharge zones, but also the precipitation of HFO.
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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".