Large Fractionation in Iron Isotopes Implicates Metabolic Pathways for Iron Cycling in Boreal Shield Lakes
Bibliographic record
Abstract
Stable Fe isotopes have only recently been measured in freshwater systems, mainly in meromictic lakes. Here we report the δ 56 Fe of dissolved, particulate, and sediment Fe in two small dimictic boreal shield headwater lakes: manipulated eutrophic Lake 227, with annual cyanobacterial blooms, and unmanipulated oligotrophic Lake 442. Within the lakes, the range in δ 56 Fe is large (ca. −0.9 to +1.8‰), spanning more than half the entire range of natural Earth surface samples. Two layers in the water column with distinctive δ 56 Fe of dissolved (dis) and particulate (spm) Fe were observed, despite differences in trophic states. In the epilimnia of both lakes, a large Δ 56 Fe dis-spm fractionation of 0.4–1‰ between dissolved and particulate Fe was only observed during cyanobacterial blooms in Lake 227, possibly regulated by selective biological uptake of isotopically light Fe by cyanobacteria. In the anoxic layers in both lakes, upward flux from sediments dominates the dissolved Fe pool with an apparent Δ 56 Fe dis-spm fractionation of −2.2 to −0.6‰. Large Δ 56 Fe dis-spm and previously published metagenome sequence data suggest active Fe cycling processes in anoxic layers, such as microaerophilic Fe(II) oxidation or photoferrotrophy, could regulate biogeochemical cycling. Large fractionation of stable Fe isotopes in these lakes provides a potential tool to probe Fe cycling and the acquisition of Fe by cyanobacteria, with relevance for understanding biogeochemical cycling of Earth’s early ferruginous oceans.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".