MétaCan
Menu
Back to cohort
Record W2792333290 · doi:10.1126/science.aao2200

Environmental dynamics during the onset of the Middle Stone Age in eastern Africa

2018· article· en· W2792333290 on OpenAlexaff
Richard Potts, Anna K. Behrensmeyer, J. Tyler Faith, Christian A. Tryon, Alison S. Brooks, John E. Yellen, Alan L. Deino, Rahab N. Kinyanjui, Jennifer B. Clark, Catherine M. Haradon, Naomi E. Levin, Hanneke J. M. Meijer, E. Grace Veatch, R. Bernhart Owen, Robin W. Renaut

Bibliographic record

VenueScience · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMiddle Stone AgeDemiseAcheuleanHomo sapiensMiddle EastGeographyArchaeologyOut of africaPangaeaStructural basinGeologyPaleontologyEthnologyPleistoceneHistoryPermian

Abstract

fetched live from OpenAlex

The Middle Stone Age in Africa The Olorgesailie basin in the southern Kenya rift valley contains sediments dating back to 1.2 million years ago, preserving a long archaeological record of human activity and environmental conditions. Three papers present the oldest East African evidence of the Middle Stone Age (MSA) and elucidate the system of technology and behavior associated with the origin of Homo sapiens . Potts et al. present evidence for the demise of Acheulean technology that preceded the MSA and describe variations in late Acheulean hominin behavior that anticipate MSA characteristics. The transition to the MSA was accompanied by turnover of large mammals and large-scale landscape change. Brooks et al. establish that ∼320,000 to 305,000 years ago, the populations in eastern Africa underwent a technological shift upon procurement of distantly sourced obsidian for toolmaking, indicating the early development of social exchange. Deino et al. provide the chronological underpinning for these discoveries. Science , this issue p. 86 , p. 90 , p. 95

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 score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.024
GPT teacher head0.260
Teacher spread0.236 · 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

Citations195
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueScienceSame topicPleistocene-Era Hominins and ArchaeologyFrench-language works237,207