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Record W1999429526 · doi:10.1073/pnas.0709372104

Evidence for declines in human population densities during the early Upper Paleolithic in western Europe

2008· article· en· W1999429526 on OpenAlexaff
Eugène Morin

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsTrent UniversityUniversité Laval
FundersNational Science Foundation
KeywordsUpper PaleolithicMiddle PaleolithicPopulationPeriod (music)GeographyRadiocarbon datingHuman evolutionContext (archaeology)EcologyEffective population sizeBiologyGenetic diversityDemographyArchaeologyPleistocene

Abstract

fetched live from OpenAlex

In western Europe, the Middle to Upper Paleolithic (M/UP) transition, dated between approximately 35,000 and approximately 40,000 radiocarbon years, corresponded to a period of major human biological and cultural changes. However, information on human population densities is scarce for that period. New faunal data from the high-resolution record of Saint-Césaire, France, indicate an episode of significant climatic deterioration during the early Upper Paleolithic (EUP), which also was associated with a reduction in mammalian species diversity. High correlations between ethnographic data and mammalian species diversity suggest that this shift decreased human population densities. Reliance on reindeer (Rangifer tarandus), a highly fluctuating resource, would also have promoted declines in human population densities. These data suggest that the EUP represented for humans a period of significant niche contraction in western Europe. In this context, the possibility that a modern human expansion occurred in this region seems low. Instead, it is suggested that population bottlenecks, genetic drift, and gene flow prevailed over human population replacement as mechanisms of evolution in humans during the EUP.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.014
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.128
GPT teacher head0.374
Teacher spread0.245 · 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

Citations82
Published2008
Admission routes1
Has abstractyes

Explore more

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