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Record W4287180323 · doi:10.5281/zenodo.4704789

Neogene hyperaridity in Arabia drove the directions of mammalian dispersal between Africa and Eurasia

2021· article· en· W4287180323 on OpenAlexaff
Madelaine Böhme, Nikolaï Spassov, Mahmoud Reza Majidifard, Andreas Gärtner, Uwe Kirscher, Michael A.W. Marks, Christian Dietzel, Gregor Uhlig, Haytham El Atfy, David R. Begun, Michael Winklhofer

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Diversity and Health Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeogeneBiological dispersalPaleontologyGeographyGeologyDemographySociology

Abstract

fetched live from OpenAlex

The evolution of the present-day African savannah fauna has been substantially influenced by the dispersal of Eurasian ancestors into Africa. The ancestors evolved endemically, together with the autochthonous taxa, into extant Afrotropical clades during the last 5 million years. However, it is unclear why Eurasian ancestors moved into Africa. Here we use sedimentological observations and soluble salt geochemical analyses of samples from a sedimentary sequence in Western Iran to develop a 10-millionyear long proxy record of Arabian climate. We identify transient periods of Arabian hyperaridity centred 8.75, 7.78, 7.50 and 6.25 million years ago, out of-phase with Northern African aridity. We propose that this relationship promoted unidirectional mammalian dispersals into Africa. This was followed by a sustained hyperarid period between 5.6 and 3.3 million years ago which impeded dispersals and allowed African mammalian faunas to endemically diversify into present-day clades. After this, the mid-Piacenzian warmth<br> enabled bi-directional fauna exchange between Africa and Eurasia, which continued during the Pleistocene.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.237
Teacher spread0.167 · 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.

Study designOther design
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

Citations0
Published2021
Admission routes1
Has abstractyes

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