Magnesium Isotope Constraints on the Holocene Hydromagnesite Formation in Alkaline Lake Dujiali, Central Qinghai‐Tibetan Plateau
Bibliographic record
Abstract
Abstract Hydromagnesite is a common hydrated magnesium carbonate mineral found in alkaline lakes on Earth, potentially present on Mars, and is also a key mineral for carbon capture and storage. However, mechanisms governing its formation in alkaline lakes remain enigmatic. Extensive hydromagnesite formed during the Holocene in the alkaline Dujiali Lake (DL), central Qinghai‐Tibetan Plateau, making it an ideal field site to constrain the process of hydromagnesite formation in a modern environmental context. In this study, we report a set of magnesium isotope ratios ( expressed as δ 26 Mg ) data from DL on abiotic hydromagnesite (mean = −1.35‰ ± 0.14‰) modern lake waters (−0.07‰ to +0.46‰), and rivers and groundwater (−0.53‰ to −1.46‰). These differences in δ 26 Mg (and also Mg/Ca) are most likely caused by low‐Mg carbonate precipitation, a process which fractionates Mg/Ca and values. A semi‐quantitative box model of the lake chemistry was developed based on carbonate equilibria to investigate the behavior of Mg isotopes during the evolution of the lake chemistry. The modeling results indicate that evaporation concentrates solutes in the lake driving saturation of multiple minerals. Aragonite reaches saturation before hydromagnesite, preferentially removing Ca relative to Mg via aragonite precipitation. This process elevates the Mg/Ca of the lake and the saturation index of hydromagnesite, increasing the likelihood of hydromagnesite formation. Given that the Mg/Ca ratio of many alkaline lakes is far below than that required for the formation of hydromagnesite, our findings suggest that low‐Mg carbonate precipitation may be a common precursor process for abiotic hydromagnesite precipitation in evaporative environments in addition to a high Mg source likely derived from ultramafic rocks.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".