The origin of rare alkali metals in geothermal fluids of southern Tibet, China: A silicon isotope perspective
Bibliographic record
Abstract
Abstract Geothermal waters from the Semi, Dagejia and Kawu hot springs in the Shiquanhe-Yarlung Zangbo geothermal field of southern Tibet (China) are highly enriched in rare alkali metals (RAM). However, the enrichment mechanism is still hotly debated. Here, we report the first silicon isotope data of these geothermal waters to unravel the origin of the extreme RAM enrichments. Sinter precipitation in the spring vents and water-rock interaction in the deep reservoir controlled both the silicon budget and silicon isotope fractionation. The rates of water-rock interaction and sinter precipitation in three spring sites decrease in the sequences Semi > Kawu > Dagejia, and Dagejia > Kawu > Semi respectively. Silicon isotope fractionation during sinter precipitation (i.e. Δ30Siprecipitate-solution < −0.1‰) is less than that due to water-rock interaction (i.e. Δ30Sisolution-rocks at least as high as −0.47‰), which makes it possible to use the δ30Si signatures of springs to evaluate the intensity of water-rock interaction. Based on the available evidence, a conceptual model of RAM enrichment is proposed: (i) persistent magmatic activity in southern Tibet provided the initial enrichment of the RAM in host rocks and a heat sources for the deep reservoirs of geothermal systems; (ii) the high Cl− content and long residence time (thousands of years) promote the leaching of RAM from the silicate host 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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".