Photoferrotrophs thrive in an Archean Ocean analogue
Bibliographic record
Abstract
Considerable discussion surrounds the potential role of anoxygenic phototrophic Fe(II)-oxidizing bacteria in both the genesis of Banded Iron Formations (BIFs) and early marine productivity. However, anoxygenic phototrophs have yet to be identified in modern environments with comparable chemistry and physical structure to the ancient Fe(II)-rich (ferruginous) oceans from which BIFs deposited. Lake Matano, Indonesia, the eighth deepest lake in the world, is such an environment. Here, sulfate is scarce (<20 micromol x liter(-1)), and it is completely removed by sulfate reduction within the deep, Fe(II)-rich chemocline. The sulfide produced is efficiently scavenged by the formation and precipitation of FeS, thereby maintaining very low sulfide concentrations within the chemocline and the deep ferruginous bottom waters. Low productivity in the surface water allows sunlight to penetrate to the >100-m-deep chemocline. Within this sulfide-poor, Fe(II)-rich, illuminated chemocline, we find a populous assemblage of anoxygenic phototrophic green sulfur bacteria (GSB). These GSB represent a large component of the Lake Matano phototrophic community, and bacteriochlorophyll e, a pigment produced by low-light-adapted GSB, is nearly as abundant as chlorophyll a in the lake's euphotic surface waters. The dearth of sulfide in the chemocline requires that the GSB are sustained by phototrophic oxidation of Fe(II), which is in abundant supply. By analogy, we propose that similar microbial communities, including populations of sulfate reducers and photoferrotrophic GSB, likely populated the chemoclines of ancient ferruginous oceans, driving the genesis of BIFs and fueling early marine productivity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".