Formation of diagenetic siderite in modern ferruginous sediments
Bibliographic record
Abstract
Ferruginous conditions prevailed in the world’s deep oceans during the Archean and Proterozoic Eons. Sedimentary iron formations deposited at that time may provide an important record of environmental conditions, yet linking the chemistry and mineralogy of these sedimentary rocks to depositional conditions remains a challenge due to a dearth of information about the processes by which minerals form in analogous modern environments. We identified siderites in ferruginous Lake Towuti, Indonesia, which we characterized using high-resolution microscopic and spectroscopic imaging combined with microchemical and geochemical analyses. We infer early diagenetic growth of siderite crystals as a response to sedimentary organic carbon degradation and the accumulation of dissolved inorganic carbon in pore waters. We suggest that siderite formation proceeds through syntaxial growth on preexisting siderite crystals, or possibly through aging of precursor carbonate green rust. Crystal growth ultimately leads to spar-sized (>50 μm) mosaic single siderite crystals that form twins, bundles, and spheroidal aggregates during burial. Early-formed carbonate was detectable through microchemical zonation and the possible presence of residual phases trapped in siderite interstices. This suggests that such microchemical zonation and mineral inclusions may be used to infer siderite growth histories in ancient sedimentary rocks including sedimentary iron formations.
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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 | 0.000 |
| 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) | 0.001 | 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".