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Record W2414564212 · doi:10.1073/pnas.1522714113

Ancient crops provide first archaeological signature of the westward Austronesian expansion

2016· article· en· W2414564212 on OpenAlexaff
Alison Crowther, Leilani Lucas, Richard Helm, Mark Horton, Ceri Shipton, Henry T. Wright, Sarah Walshaw, Matthew Pawlowicz, Chantal Radimilahy, Katerina Douka, Llorenç Picornell-Gelabert, Dorian Q. Fuller, Nicole Boivin

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsSimon Fraser University
FundersNatural Environment Research CouncilSight Research UKTanzania Commission for Science and TechnologyEuropean Research CouncilBritish AcademyNational Science Foundation
KeywordsArchaeologySignature (topology)GeographyAustronesian languagesAncient historyGeologyHistoryLinguistics

Abstract

fetched live from OpenAlex

The Austronesian settlement of the remote island of Madagascar remains one of the great puzzles of Indo-Pacific prehistory. Although linguistic, ethnographic, and genetic evidence points clearly to a colonization of Madagascar by Austronesian language-speaking people from Island Southeast Asia, decades of archaeological research have failed to locate evidence for a Southeast Asian signature in the island's early material record. Here, we present new archaeobotanical data that show that Southeast Asian settlers brought Asian crops with them when they settled in Africa. These crops provide the first, to our knowledge, reliable archaeological window into the Southeast Asian colonization of Madagascar. They additionally suggest that initial Southeast Asian settlement in Africa was not limited to Madagascar, but also extended to the Comoros. Archaeobotanical data may support a model of indirect Austronesian colonization of Madagascar from the Comoros and/or elsewhere in eastern Africa.

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 score0.004
Threshold uncertainty score0.011

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)0.0030.000

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 head0.044
GPT teacher head0.308
Teacher spread0.264 · 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

Citations283
Published2016
Admission routes1
Has abstractyes

Explore more

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