Trace Elemental Partitioning on Clays Derived From Hydrothermal Muds of the El Tatio Geyser Field, Chile
Bibliographic record
Abstract
Abstract Recent experimental studies have demonstrated that clay minerals (e.g., kaolinite, illite, and montmorillonite) have higher affinities for some trace elements under acidic versus alkaline conditions. This suggests that clays might be important vectors in the transport of trace elements from sites of acidic chemical weathering on land to marine depositional environments. To determine if clays behave similarly in nature, we collected water and mud (consisting of 38.5%–61.1% of kaolinite and montmorillonite) samples from boiling, low‐pH, mud pools venting at the El Tatio Geyser Field (ETGF) in Chile. Based on elemental abundances in the aqueous/solid phases, we observed that mud samples collected from lower pH pools (e.g., pH = 2.42 and 3.55) have high concentration factors for anionic elements (e.g., P and As) but low concentration factors for cationic elements (e.g., Ca, Mn, and Sr), while mud samples from higher pH pools (e.g., pH = 4.87 and 5.84) display the opposite trend. Acid‐base leaching experiments further reveal that increasing solution pH (to reflect downstream transport) led to the release of As and P from the mud surfaces due to increasingly negative surface charge, while decreasing pH (to determine the effects of re‐acidification) released Li, Ca, Co, Sr, Mo, and Cd. Our study confirms previous experimental findings that demonstrate clay minerals can assemble a diverse inventory of trace elements during acid weathering (e.g., As) but then liberate them back into the aqueous phase as aqueous pH increases. Importantly, these observations provide a mechanism to account for the previous observations of regional As contamination in rivers downstream of the ETGF.
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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) | 0.001 | 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".