Photoferrotrophy, BIF deposition, and iron-free oxygen oases in the Archean surface ocean
Bibliographic record
Abstract
Abstract Banded Iron Formations (BIFs) are both the world’s largest ore deposits and important geological archives that record the early evolution of the Earth-Life system. BIFs were likely deposited as the result of ferrous iron [Fe(II)] oxidation, precipitation, and sedimentation from iron-rich (ferruginous) seawater, mostly during the Archean Eon. Proposed mechanisms for iron oxidation include abiotic reactions with photosynthetic oxygen, reaction with oxygen catalyzed by iron-oxidizing bacteria (IOB), and anoxic oxidation by anoxygenic iron-oxidizing phototrophic bacteria (photoferrotrophs). These iron oxidation processes may have operated concurrently, but their relative contributions to BIF deposition have not been considered. Here, we developed a 1-D ferruginous ocean model incorporating abiotic iron cycling and the physiology of oxygenic phototrophs, microaerophilic IOB, photoferrotrophs, and iron-reducing bacteria. Our model shows that, under Archean ocean conditions, most iron oxidation and precipitation would have been driven by photoferrotrophy, with a small fraction by microaerophilic IOB and a negligible contribution from abiotic reactions. The combined activities of these pathways led to BIF deposition at rates in line with geological records and, importantly, allowed the development of an Fe(II)-free surface ocean conducive to the formation of oxygen oases and the proliferation of oxygenic phototrophs. Teaser Archean ocean simulation shows that photoferrotrophs dominated the precipitation of BIFs and promoted the formation of marine oxygen oases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".