Labyrinth patterns in Magadi (Kenya) cherts: Evidence for early formation from siliceous gels
Bibliographic record
Abstract
Abstract Sedimentary cherts, with well-preserved microfossils, are known from the Archean to the present, yet their origins remain poorly understood. Lake Magadi, Kenya, has been used as a modern analog system for understanding the origins of nonbiogenic chert. We present evidence for synsedimentary formation of Magadi cherts directly from siliceous gels. Petrographic thin-section analysis and field-emission scanning electron microscopy of cherts from cores drilled in Lake Magadi during the Hominin Sites and Paleolakes Drilling Project in 2014 led to the discovery of two-dimensional branching “labyrinth patterns” in chert, which are a type of fractal “squeeze” pattern formed at air-liquid interfaces. Labyrinth patterns preserved in chert from Lake Magadi cores indicate invasion of air along planes in dewatering gels. These patterns support the precipitation of silica gels in the saline-alkaline Lake Magadi system and syndepositional drying of gels in contact with air as part of chert formation. Recognizing cherts as syndepositional has been critical for our use of them for U-Th dating. Identification of labyrinth patterns in ancient cherts can provide a better understanding of paleoenvironmental and geochemical conditions in the past.
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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 | 0.000 | 0.001 |
| 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".