Insights on the Paleoclimate and Paleoecology of an Early Miocene Hominoid Site: A Multiproxy Study From Koru, Western Kenya
Bibliographic record
Abstract
Abstract Early Miocene terrestrial ecosystems in eastern Africa were shaped by regional rifting, local, regional, and global climate change, and biogeography, which in turn influenced the evolution of hominoids and other vertebrates. Here, we present a multiproxy study focused on reconstructing the ecosystem structure and climate of the Koru 16 locality, which is a fossil‐rich Early Miocene (∼20 Ma) site in Kisumu County, Kenya. At Koru 16, the lithofacies consist of interbedded ash and weakly developed paleosols, indicating episodic volcanic disturbances from the nearby Tinderet volcanic complex. Paleosol features and elemental weathering estimates suggest warm, wet conditions. Over 1,000 fossil leaves from two quarries (∼5 m apart) yielded 18 morphotypes, with 55% of the morphotypes found at both quarries, reflecting local landscape heterogeneity. Leaf physiognomic methods estimate mean annual precipitation at ∼2,000 mm/yr and a mean annual temperature >25°C indicating a tropical climate. Leaf lifespan reconstructions suggest a semi‐deciduous forest. Leaf mass per area values align with modern tropical rainforests and seasonal forests, corroborated by tree stump casts indicating a frequently disturbed forest with patches of closed‐ and open‐canopy similar to modern primate‐supporting tropical forests. The vertebrate fauna included a medium‐sized pythonid, three ape species, and other typical Early Miocene mammals. This multiproxy result indicates that seasonally wet tropical forest environments played a role in the evolution of Early Miocene vertebrate communities and emphasizes the importance of site‐specific studies in assessing habitat heterogeneity in Early Miocene hominoid ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".