Spatial and temporal geochemical variability in lacustrine sedimentation in the East African Rift System: Evidence from the Kenya Rift and regional analyses
Bibliographic record
Abstract
Abstract Many previous studies on lacustrine basins in the East African Rift System have directed their attention to climatic controls on contemporary sedimentation or climate change as part of palaeoenvironmental reconstruction. In contrast, this research focuses on the impact of tectonism and volcanism on rift deposition and develops models that help to explain their roles and relative importance. The study focuses on the spatial and temporal variability in bulk sediment geochemistry from a diverse range of modern and ancient rift sediments through an analysis of 519 samples and 50 major and trace elements. The basins examined variously include, or have contained, wetlands and/or shallow to deep, fresh to hypersaline lakes. Substantial spatial variability is documented for Holocene to modern deposits in lakes Turkana, Baringo, Bogoria, Magadi and Malawi. Mio‐Pleistocene sediments in the Central Kenya Rift and Quaternary deposits of the southern Kenya Rift illustrate temporal variability. Tectonic and volcanic controls on geochemical variability are explained in terms of: (i) primary controlling factors (faulting, subsidence, uplift, volcanism, magma evolution and antecedent lithologies and landscapes); (ii) secondary controls (bedrock types, rift shoulder and axis elevations, accommodation space, meteoric and hydrothermal fluids and mantle CO2); and (iii) response factors (catchment area size, orographic rains, rain shadows, vegetation densities, erosion and weathering rates, and spring/runoff ratios). The models developed have, in turn, important implications for palaeoenvironmental interpretation in other depositional basins.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".