Cryptic iron cycling influenced by organic carbon availability in a seasonally stratified lake
Bibliographic record
Abstract
Iron cycling including phototrophic Fe(II) oxidation has been observed in multiple permanently stratified meromictic lakes, yet less focus has been on dimictic lakes, which seasonally overturn and are vastly more common. Here, we investigated iron cycling in a dimictic lake, Großes Heiliges Meer in northwest Germany, using 16S rRNA amplicon sequencing, as well as in-situ and lab-based experiments. Bacterial community composition in the lake follows geochemical gradients and differs markedly between oxic and anoxic conditions. Potential iron-metabolizing bacteria were found mostly in anoxic conditions at 7 and 8 m depth and were comprised of taxa from the genera Chlorobium, Thiodictyon, Sideroxydans, Geobacter, and Rhodoferrax. We were able to recreate active iron cycling (1) with an ex-situ microbial community from 8 m depth and (2) with a successful microbial enrichment culture from 7 m depth. Varying the light and organic carbon availability in lab-based experiments showed that Fe(III) reduction overshadows Fe(II) oxidation leading to a cryptic iron cycle. Overall, we could demonstrate that microbial iron cycling can be a key biogeochemical process in dimictic lakes despite regular disturbance, and that complex environmental factors such as organic substrates control the balance between Fe(II) oxidation and Fe(III) reduction.
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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".