The Formation of Clay Minerals in the Mudflats of Bolivian Salars
Bibliographic record
Abstract
Abstract Understanding clay-mineral assemblages forming in saline lakes aids in reconstructing paleoenvironments on Earth and other terrestrial planets; this is because authigenic phyllosilicates are sensitive to the prevailing geochemical conditions present during formation. In most geochemical models, evaporative concentration favors sepiolite with increasing silica and Mg2+ concentrations without considering the role of the biogenic removal of silica from solution by diatoms. In the present study, phyllosilicates occurring in the mudflats of Bolivian salars were investigated to aid in understanding the geochemical factors that control mineral assemblages forming in (SO42–)- and (Cl–)-rich environments in relation to dissolved silica. From transects across the mudflats, the physical, chemical, and mineralogical characteristics of the bulk sediment and the <2 μm fraction of each sedimentary layer were analyzed. From these analyses, three types of sediments were identified: (1) regolith sediments dominated by Al-dioctahedral smectite, illite, and chlorite; (2) detritus-rich mudflat sediments with Mg-trioctahedral smectite and Al-dioctahedral smectite along with illite and chlorite; and (3) authigenic mudflat sediments dominated by poorly formed Mg-trioctahedral smectite, kerolite, and biogenic silica. The absence of sepiolite-palygorskite in the salars is the result of excessively high Mg:Si ratios within the waters. In the surface water Mg becomes enriched relative to Si as diatoms remove dissolved Si from solution through biologically mediated uptake. The geochemical conditions present within the salars that act to preserve the diatom frustules and prevent their dissolution include: neutral–slightly alkaline pH solutions, cold temperatures, shallow water, and high salinity. Under these conditions the formation of sepiolite is restricted by the small amount of dissolved silica, despite the silica-rich environment. The formation of Mg-smectite and kerolite is favored under these conditions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".