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Record W4365445770 · doi:10.1126/science.abq2834

Oldest evidence of abundant C <sub>4</sub> grasses and habitat heterogeneity in eastern Africa

2023· article· en· W4365445770 on OpenAlexaff
Daniel J. Peppe, Susanne Cote, Alan L. Deino, David L. Fox, John D. Kingston, Rahab N. Kinyanjui, William E. Lukens, Laura MacLatchy, Alice Novello, Caroline A. E. Strömberg, Steven G. Driese, Nicole Garrett, Kayla R. Hillis, Bonnie F. Jacobs, Kirsten Jenkins, Robert Kityo, Thomas Lehmann, Fredrick K. Manthi, Emma Mbua, Lauren A. Michel, Ellen R. Miller, Amon A. T. Mugume, Samuel Muteti, Isaiah Nengo, Kennedy Ogonda Oginga, Samuel R. Phelps, P. J. Polissar, James B. Rossie, Nancy J. Stevens, Kevin T. Uno, Kieran P. McNulty

Bibliographic record

VenueScience · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGrasslandHabitatEcologyMammalVegetation (pathology)EcosystemBiomass (ecology)GeographyLimitingBiology

Abstract

fetched live from OpenAlex

The assembly of Africa’s iconic C 4 grassland ecosystems is central to evolutionary interpretations of many mammal lineages, including hominins. C 4 grasses are thought to have become ecologically dominant in Africa only after 10 million years ago (Ma). However, paleobotanical records older than 10 Ma are sparse, limiting assessment of the timing and nature of C 4 biomass expansion. This study uses a multiproxy design to document vegetation structure from nine Early Miocene mammal site complexes across eastern Africa. Results demonstrate that between ~21 and 16 Ma, C 4 grasses were locally abundant, contributing to heterogeneous habitats ranging from forests to wooded grasslands. These data push back the oldest evidence of C 4 grass–dominated habitats in Africa—and globally—by more than 10 million years, calling for revised paleoecological interpretations of mammalian evolution.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.065
GPT teacher head0.338
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations67
Published2023
Admission routes1
Has abstractyes

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