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Record W2953118425 · doi:10.1002/evan.21787

Hybridization in human evolution: Insights from other organisms

2019· review· en· W2953118425 on OpenAlexaff
R. Ackermann, Michael L. Arnold, Marcella D. Baiz, James A. Cahill, Liliana Cortés‐Ortiz, Ben J. Evans, B. Rosemary Grant, Peter R. Grant, Benedikt Hallgrímsson, Robyn Humphreys, Clifford J. Jolly, Joanna Malukiewicz, Christopher J. Percival, Terrence B. Ritzman, Christian Roos, Charles C. Roseman, Lauren Schroeder, Fred H. Smith, Kerryn Warren, Robert K. Wayne, Dietmar Zinner

Bibliographic record

VenueEvolutionary Anthropology Issues News and Reviews · 2019
Typereview
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsAlberta Children's HospitalUniversity of CalgaryUniversity of TorontoMcMaster University
FundersNational Institute of General Medical SciencesDepartment of Science and Technology, Ministry of Science and Technology, IndiaNational Research Foundation
KeywordsBiologyEvolutionary biologyContext (archaeology)Adaptation (eye)Variation (astronomy)Adaptive evolutionHuman evolutionRange (aeronautics)PhenotypeEvolutionary dynamicsDiversity (politics)GeneGeneticsSociologyPaleontologyPopulation

Abstract

fetched live from OpenAlex

During the late Pleistocene, isolated lineages of hominins exchanged genes thus influencing genomic variation in humans in both the past and present. However, the dynamics of this genetic exchange and associated phenotypic consequences through time remain poorly understood. Gene exchange across divergent lineages can result in myriad outcomes arising from these dynamics and the environmental conditions under which it occurs. Here we draw from our collective research across various organisms, illustrating some of the ways in which gene exchange can structure genomic/phenotypic diversity within/among species. We present a range of examples relevant to questions about the evolution of hominins. These examples are not meant to be exhaustive, but rather illustrative of the diverse evolutionary causes/consequences of hybridization, highlighting potential drivers of human evolution in the context of hybridization including: influences on adaptive evolution, climate change, developmental systems, sex-differences in behavior, Haldane's rule and the large X-effect, and transgressive phenotypic variation.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.064
GPT teacher head0.394
Teacher spread0.330 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations108
Published2019
Admission routes1
Has abstractyes

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