Signatures of biologically influenced CaCo<sub>3</sub> and Mg–Fe silicate precipitation in hot springs: Case study from the Ruidian geothermal area, western Yunnan Province, China
Bibliographic record
Abstract
Abstract Hot springs at Gongxiaoshe and Zhuyuan (maximum temperatures of 73 to 84°C, respectively) are characterized by deposits formed of calcite, aragonite, non‐crystalline Si–Mg–Fe deposits, and minor amounts of barite and gypsum. The deposits at Gongxiaoshe are formed largely of alternating calcite and aragonite laminae, whereas those at Zhuyuan are formed largely of calcite. The calcite is in the form of: (i) pseudodendrites that grew as sub‐crystals stacked upon each other; and (ii) unattached euhedral and incompletely formed dodecahedral and rhombohedral crystals. Amorphous Ca CO 3 , formed of nanoparticles <1 μm long, is common in some of the Zhuyuan deposits, but minor in the Gongxiaoshe deposits. The morphologically diverse arrays of aragonite crystals that lie parallel to bedding were not nucleated on a growth surface. Many substrates in these deposits are covered with reticulate coatings that are formed largely of Si and Mg with minor Fe and micro‐granular coatings that are formed largely of Si and Fe. Biofilms, with their extracellular polymeric substances, and microbes are common at both springs. The compositionally and crystallographically diverse precipitates at these two springs are attributed to a biologically influenced model with precipitation taking place in micro‐domains that developed in the extracellular polymeric substances. According to this model, precipitation varied at the micron‐scale influenced by the elemental concentrations that developed in the hydrogel of extracellular polymeric substances. Critically, the very low preservation potential of the extracellular polymeric substance and its formative microbes means that the precipitates will rapidly lose evidence of their biotic origin. The compositional diversity of the precipitates, the crystallographic diversity of the calcite and aragonite with numerous incompletely formed crystals, and local concentrations of Si, Mg and Fe may, however, serve as proxies of that biologically influenced precipitation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".