Interactions between iron and carbon in permafrost thaw ponds
Bibliographic record
Abstract
Thawing permafrost forms "thaw ponds" that accumulate and transport organic carbon (OC), redox-active iron (Fe), and other elements. Although Fe has been shown to act as a control on the microbial degradation of OC in permafrost soils, the role of iron in carbon cycling in thaw ponds remains poorly understood. Here, we investigated Fe-OC interactions in thaw ponds in partially and fully thawed soils ("bog" and "fen" thaw ponds, respectively) in a permafrost peatland complex in Abisko, Sweden, using size separation (large particulate fraction (LPF), small particulate fraction (SPF), and dissolved fraction (DF)), acid extractions, scanning electron microscopy (SEM), Fe K-edge X-ray absorption spectroscopy (XAS), and Fourier Transform Infrared (FTIR) spectroscopy. The bulk total Fe (total suspended Fe) in the bogs ranged from 135 mg/L (mean = 13 mg/L) whereas the fens exhibited higher total Fe (1.5 to 212 mg/L, mean = 30 mg/L). The concentration of bulk total OC (TOC) in the bog thaw ponds ranged from 50 to 352 mg/L (mean = 170 mg/L), higher than the TOC concentration in the fen thaw ponds (8.5 to 268 mg/L, mean = 17 mg/L). The concentration of 1 M HCl-extractable Fe in the bog ponds was slightly lower than that in the fens (93 ± 1.2 and 137 ± 3.5 mg/L Fe, respectively) with Fe predominantly (>75 %) in the DF in both thaw stages. Fe K-edge XAS analysis showed that while Fe(II) was the predominant species in LPF, Fe(III) was more abundant in the DF, indicating that the stage of thawing and particle size may control Fe redox state. Furthermore, Fe(II) and Fe(III) were partially complexed with natural organic matter (NOM, 8 to 80 %) in both thaw ponds. Results of our work suggest that Fe and OC released during permafrost thaw into thaw ponds (re-)associate, potentially protecting OC from microbial decomposition while also stabilizing the redox state of Fe.
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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".