Trace-Element Composition of Cherts from Alkaline Lakes in the East African Rift
Bibliographic record
Abstract
Abstract Magadiite and cherts from the Magadi basin in southern Kenya and four other localities in the East African Rift all share distinctive compositional systematics: (1) negatively fractionated REE patterns; (2) high absolute concentrations of U, Nb, and Zr (up to 1,500 ppm versus a crustal average of 190 ppm); (3) normalized enrichments of U, Nb, and Zr relative to REE; (4) extreme fractionations of U-Th, Nb-Ta, and Zr-Hf; (5) positive Ce but negative Eu anomalies; and (6) normalized peaks at Mo, Ag, and Sb. Alkaline lake cherts composed of secondary quartz retain the trace-element patterns of the precursor, albeit at lower absolute element contents. Sublacustrine sinters at Lake Bogoria share some of the compositional features of the cherts from Magadi but lack the Ce and Eu anomalies and U-Th, Nb-Ta, and Zr-Hf fractionations. Cherts from ocean-ridge, pelagic, and continental-shelf settings are characterized by progressively larger Zr contents (≤ 170 ppm), but all are distinctly lower than in the alkaline-lake cherts, where micron-scale Zr-rich phases were identified as possible authigenic zircons. Both marine and alkaline-lake cherts share positive Ce anomalies. These result from scavenging of Ce by Fe-Mn oxyhydroxides in seawater, but from the solubility of Ce (IV) in oxidized alkaline brines for the lacustrine cherts. Europium anomalies range from small negative to positive in marine cherts, whereas alkaline-lake cherts feature large negative anomalies. These compositional systematics, which reflect the aqueous environment in which the chert precursor formed, confer a tool for interpreting the paleoenvironment of cherts in the geological record.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".