Organic matter preservation through complexation with iron minerals in two basins of a dimictic boreal lake with contrasting deep water redox regimes
Bibliographic record
Abstract
The biogeochemical cycles of iron and organic carbon (OC) are closely interconnected in terrestrial and aquatic systems. In ocean waters, the concentration of reactive Fe is tightly controlled by soluble organic ligands. In soils, Fe stabilizes OC by forming aggregates that shield OC from degradation. In lake sediments however, the role of Fe in the preservation of OC has not been explored as extensively yet. We investigated Fe-OC interactions in sediment collected from Lake Tantaré, in which two basins are characterized by contrasting redox conditions. These contrasting redox conditions provide an opportunity to assess their importance in the formation of stable Fe-OC complexes. On average, 30.1 ± 6.4 % of total OC was liberated upon reductively dissolving reactive iron. The Fe-associated and the non-Fe-associated OC pools were characterized at the elemental (OC, TN), isotopic (δ13C, δ15N) and functional group (FTIR) levels. Large differences in OC:Fe and TN:Fe ratios between the two basins were found which were not linked to OM chemical composition but rather to differences in reactive iron concentrations stemming from the higher abundance of iron sulfides in the anoxic basin. Nevertheless, since the affinity of OM for iron sulfides is lower than that for iron hydroxides, using OC:Fe and TN:Fe ratios as a diagnostic tool for the type of OM-Fe interactions should be done with care in anoxic environment. Same caution should be considered for oxic sediments due to the variation of the proportion of iron hydroxides associated with OM from sample to sample.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".