Silica phase transformations drive Si-O isotopic offsets in ancient chert: Implications for paleo-ocean temperature and silicon cycle reconstructions
Bibliographic record
Abstract
The datasets include major and minor elements, and TOC contents (wt.%; Supplementary Data Table S1), trace element and REEs concentrations (mg/kg; Supplementary Data Table S2). Various chert types from representative sedimentary profiles of the Paleozoic, Proterozoic and Archean, invoking volcanic, S-, C- (seawater, hydrothermal), dike chert types and their Si and O isotopic and trace elements datasets (Supplementary Data Table S3), and Si and O in-situ isotope datasets for representative microquartz and quartz veins of the 1.88 Ga Gunflint (Canada), 2.72 Ga Tumbiana Formation (Australia), 3.29 Ga Mendon Formation (South Africa), and 3.48 Ga Dresser Formation (Australia) chert (Supplementary Data Table S4). Also included are Si isotope fractionation in opal-CT dissolves and reprecipitates to microquartz process in porewater (Supplementary Data Table S5), and models for various styles of paleotemperature reconstruction from O isotope of chert (Supplementary Data Table S6) are shown for the early Silurian C-chert (seawater chert) sample from the Yangtze block, South China, the Proterozoic-Cambrian transition C-chert from the Bohemian Massif, Czech Republic, and 1.9 Ga Gunflint chert, Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".