Quantifying the contribution of biogenic iron sulfides to magnesite formation in the Basque Lakes, British Columbia, Canada
Bibliographic record
Abstract
Understanding terrestrial carbon cycling is important to develop carbon dioxide removal (CDR) technologies. Neutralization of carbonic acid (H 2 CO 3 ) to make carbonate minerals provides a secure, long-term sink for CO 2 in natural and anthropogenic systems. In the sediment, dissimilarly sulfate reduction has the potential to generate alkalinity and induce biogenic precipitation of carbonate minerals, however, the proportion of Fe-sulfides to carbonate minerals is rarely quantified. Saline and hypersaline lakes containing carbonate minerals are excellent environments to examine closed system geochemical cycles of carbon, sulfur and iron. Here, we quantified the contribution of biogenic Fe-sulfide minerals and buried organic carbon to magnesite (MgCO 3 ) formation in the sediment of Basque Lake #2, a Mg-Na-SO 4 hypersaline lake near Ashcroft, British Columbia, Canada. While the overlying surface water contains over 2 M SO 4 2− , the total solid iron sulfide content of the sediment was <0.4 wt%. The Basque Lake #2 sediment core had much more abundant carbonate minerals than sulfide minerals, suggesting the biogenic processes that form Fe-sulfides have a minimal role in magnesite formation (<1.0 wt%). Laboratory experiments were also conducted to study the relative influence of microbial sulfate and iron reduction on alkalinity generation and biogenic mineral formation with different organic carbon amendments. Filter-sterilized sulfate-rich waters (∼800 mM SO 4 2− ) from Basque Lake #1 were inoculated with anoxic Basque Lake sediment and microbial communities with or without the addition of ferrihydrite [Fe 10 III O 14 (OH) 2 ]. Biogenic magnetite (Fe III 2 Fe II O 4 ) and mackinawite (Fe II S) formed when ferrihydrite was added to microbial experiments and siderite (Fe II CO 3 ) precipitated in the lactate-amended experiment. The bulk analysis techniques used in these laboratory experiments suggest there is minimal to no additional magnesite precipitation. This study highlights that in closed systems, such as Basque Lake #2, the role of dissimilatory sulfate reduction in net carbonate precipitation will be limited by ferric iron input into lakes, which therefore inhibits biogenic Fe-sulfide formation, even with extremely high aqueous sulfate concentrations. Ultimately, it is unlikely that sulfate and iron reduction play important roles in Mg‑carbonate formation within Fe-limited environments. • Basque #2 lake sediment is composed of magnesite, gypsum and Mg-sulfate minerals. • Only ∼1 % of magnesite is due to precipitation of biogenic Fe-sulfide minerals. • Organic carbon amendments to Basque sediment resulted in an alkalinity increase. • Ability of dissimilatory sulfate reduction to capture CO 2 as carbonates is limited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".