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Record W2510480879 · doi:10.1073/pnas.1607493113

Spiny plants, mammal browsers, and the origin of African savannas

2016· article· en· W2510480879 on OpenAlexaff
Tristan Charles‐Dominique, T. Jonathan Davies, Gareth P. Hempson, Bezeng S. Bezeng, Barnabas H. Daru, R.M. Kabongo, Olivier Maurin, A. Muthama Muasya, Michelle van der Bank, William J. Bond

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsMcGill University
FundersMedical Research CouncilCambridge Overseas TrustBritish Pharmacological Society
KeywordsMammalBiologyEcologyConvergent evolutionDiversification (marketing strategy)Evolutionary biologyPhylogenetics

Abstract

fetched live from OpenAlex

Savannas first began to spread across Africa during the Miocene. A major hypothesis for explaining this vegetation change is the increase in C4 grasses, promoting fire. We investigated whether mammals could also have contributed to savanna expansion by using spinescence as a marker of mammal herbivory. Looking at the present distribution of 1,852 tree species, we established that spinescence is mainly associated with two functional types of mammals: large browsers and medium-sized mixed feeders. Using a dated phylogeny for the same tree species, we found that spinescence evolved at least 55 times. The diversification of spiny plants occurred long after the evolution of Afrotherian proboscideans and hyracoids. However, it is remarkably congruent with diversification of bovids, the lineage including the antelope that predominantly browse these plants today. Our findings suggest that herbivore-adapted savannas evolved several million years before fire-maintained savannas and probably, in different environmental conditions. Spiny savannas with abundant mammal herbivores occur in drier climates and on nutrient-rich soils, whereas fire-maintained savannas occur in wetter climates on nutrient-poor soils.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

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

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 head0.068
GPT teacher head0.255
Teacher spread0.187 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations188
Published2016
Admission routes1
Has abstractyes

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