MétaCan
Menu
Back to cohort
Record W4319227700 · doi:10.1038/s42003-023-04510-0

Prehistoric human migration between Sundaland and South Asia was driven by sea-level rise

2023· article· en· W4319227700 on OpenAlexaff
Hie Lim Kim, Tanghua Li, Namrata Kalsi, Hung Nguyen, Timothy A. Shaw, K.C. Ang, Keith C. Cheng, Aakrosh Ratan, W. R. Peltier, Dhrubajyoti Samanta, Mahesh Pratapneni, Stephan C. Schuster, Benjamin P. Horton

Bibliographic record

VenueCommunications Biology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsUniversity of Toronto
FundersNational Supercomputing Centre SingaporeEarth Observatory of SingaporeMinistry of Education, IndiaUniversity of Hong KongNational Research FoundationMinistry of Education - SingaporeNational Research Foundation Singapore
KeywordsLast Glacial MaximumHuman migrationPrehistoryGeographyHoloceneMeltwaterPopulationSea levelPhysical geographyOceanographyArchaeologyGeologyDemographyGlacier

Abstract

fetched live from OpenAlex

Rapid sea-level rise between the Last Glacial Maximum (LGM) and the mid-Holocene transformed the Southeast Asian coastal landscape, but the impact on human demography remains unclear. Here, we create a paleogeographic map, focusing on sea-level changes during the period spanning the LGM to the present-day and infer the human population history in Southeast and South Asia using 763 high-coverage whole-genome sequencing datasets from 59 ethnic groups. We show that sea-level rise, in particular meltwater pulses 1 A (MWP1A, ~14,500-14,000 years ago) and 1B (MWP1B, ~11,500-11,000 years ago), reduced land area by over 50% since the LGM, resulting in segregation of local human populations. Following periods of rapid sea-level rises, population pressure drove the migration of Malaysian Negritos into South Asia. Integrated paleogeographic and population genomic analysis demonstrates the earliest documented instance of forced human migration driven by sea-level rise.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.405

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 studies0.0000.000
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 head0.067
GPT teacher head0.351
Teacher spread0.283 · 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 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

Citations23
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCommunications BiologySame topicForensic and Genetic ResearchFrench-language works237,207