Iron Mineralogy and Sediment Color in a 100 m Drill Core From Lake Towuti, Indonesia Reflect Catchment and Diagenetic Conditions
Bibliographic record
Abstract
Abstract Iron is the most abundant redox‐sensitive element on the Earth's surface, and the oxidation state, mineral host, and crystallinity of Fe‐rich phases in sedimentary systems can record details of water‐rock interactions and environmental conditions. However, we lack a complete understanding of how these Fe‐rich materials are created, maintained, and oxidized or reduced in sedimentary environments, particularly those with mafic sources. The catchment of Lake Towuti, Indonesia, is known to contain a wide range of abundant crystalline Fe oxide, and the lake has a long sedimentary history. Here, we study a ∼100 m long drill core from the lake to understand patterns of sedimentation and how young iron‐rich sediments are affected by diagenesis through geologic time. We use visible/near infrared and Mössbauer spectroscopy, X‐ray diffraction, bulk chemistry measurements, and statistical cluster analysis to characterize the core sediment. We find that the core sediment can be divided into three statistically different zones dominated by Mg serpentine, Al clay minerals, and Fe 2+ carbonate, respectively. The entire core is rich in nanophase Fe, and elemental correlations and Fe mineralogy vary between these zones. The nanophase Fe is highly complex with both ferrous and ferric components, and contributes to, but does not dictate, variations in sediment color. We propose that the distinctive zones are the result of structural basin changes (notably river capture and shifting drainage patterns), and diagenetic overprinting caused by deep burial of reactive Fe. This complex record has implications for disentangling depositional and diagenetic trends in other mafic lacustrine systems.
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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 | 0.000 | 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".