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Record W2807867457 · doi:10.1038/s41598-018-27647-z

The Emergence of Animal Management in the Southern Levant

2018· article· en· W2807867457 on OpenAlexaff
Natalie D. Munro, Guy Bar‐Oz, Jacqueline Meier, Lidar Sapir‐Hen, Mary C. Stiner, Reuven Yeshurun

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsTrent University
FundersIsrael Science FoundationHebrew University of JerusalemNational Science Foundation
KeywordsUngulateSouthern LevantDomesticationGeographyZooarchaeologyArchaeologyMediterranean climateMiddle EastEcologyBiologyHabitat

Abstract

fetched live from OpenAlex

Our compilation of zooarchaeological data from a series of important archaeological sites spanning the Epipaleolithic through Pre-Pottery Neolithic B periods in the Mediterranean Hills of the southern Levant contributes to major debates about the beginnings of ungulate management in Southwest Asia. The data support an onset of ungulate management practices by the Early PPNB (10,500-10,000 cal. BP), more than 500 years earlier than previously thought for this region. There is a clear developmental connection between reduced hunting intensity and the uptake of ungulate management, confirming that this process began in response to local, density-dependent demographic factors. The early process of goat domestication in the southern Levant appears to have been overwhelmingly local. This may have been true for cattle and pigs as well. Nevertheless, the loose synchrony of animal management trends across Southwest Asia was undoubtedly enabled by large-scale social networks that transmitted knowledge. The results add to growing evidence that animal management processes followed multiple regional evolutionary pathways within the Fertile Crescent.

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)0.001
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.015
GPT teacher head0.219
Teacher spread0.204 · 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

Citations103
Published2018
Admission routes1
Has abstractyes

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