Analysis of Clay Minerals in the Mudflats of Sulfate-Rich Saline Lakes
Bibliographic record
Abstract
Clay minerals are important targets for interpreting past environments due to their ubiquity on the surface of terrestrial planets, sensitivity to the prevailing geochemical conditions, and ability to preserve biosignatures. Modern saline lakes that develop sulfate-rich brines are less extensively analyzed due to their relative scarcity compared to those that develop bicarbonate-rich brines. Moreover, most modern sulfate-rich systems are small in area (<100 km2), playa dominated, and rich in detritus making them difficult to sample and to study. Yet, the number and diversity of these lakes makes them important for understanding authigenic mineral formation. The goal of this study was to evaluate authigenic and detrital minerals in the mudflats of sulfate-rich saline lakes. This research focused on microscale (cm) investigations of the mudflats from 23 lakes from the Great Plains of southern Saskatchewan, Canada, and Andean Plateau, Bolivia for a total of 407 sediment samples. For detrital-rich sediments, authigenic illite was consistently found within sand lenses of the mudflats, was rich in Al, Mg, and Fe, and displayed lath-like morphologies. Uncommon minerals were also found containing elements usually in minor/trace quantities that concentrate in the closed-basins. In Laguna Chiar Khota, Bolivia for example, the arsenic-rich minerals realgar and hörnesite were found within the mudflats. Lastly, this thesis demonstrates that the biologically-mediated uptake of silica by diatoms can shift the geochemical parameters within the lakes, where Mg-smectite form instead of sepiolite in an otherwise silica-rich environment. Overall, this research contributes to our understanding of authigenic clay formation recognizing similarities between these highly diverse environments. In detrital-rich environments where authigenic minerals are difficult to identify, such as the deltaic-lacustrine deposits in Gale crater, Mars, this study shows that authigenic illite can be found within the sand-lenses of the mudflats. Moreover, this study also shows that we need to consider the factors of diatom dissolution with respect to dissolved silica. The influence of diatoms on the phyllosilicates in brackish lakes suggests we may need to examine the modes of formation for sepiolite in ancient deposits, which may be forming from post depositional alteration of the lacustrine sediments.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".