The role of Fe(II)-silicate gel in the generation of Archean and Paleoproterozoic chert
Bibliographic record
Abstract
Abstract Chert is abundant in Archean and Paleoproterozoic rocks and is commonly densely packed with authigenic Fe(II)-silicate nanoparticles such as greenalite, indicating a close relationship between iron and silica deposition. We investigate the relationship between Fe(II)-silicate minerals and dissolved silica during precipitation, settling, and diagenesis using anoxic synthesis, sorption, and heating experiments. Excess silica is associated with the solid during precipitation, resulting in high molar Si/Fe ratios (<1.52) that exceed that of stoichiometric greenalite (0.67). At pH 8–8.5, silica sorbs to the surface, reaching sorption densities of 0.68 mmol Si per mmol Fe(II)-silicate. Furthermore, excess Si is released upon heating as the Fe(II)-silicate gel crystallizes. We suggest that Fe(II)-silicate minerals acted as an effective Si shuttle between the water column and the sediments in Archean and Paleoproterozoic marine environments, providing sites for the growth of early diagenetic chert, consistent with observations from the sedimentary record. Our results explain the exceptional preservation of greenalite in early chert and indicate that these minerals could provide a robust archive of marine geochemical data.
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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".