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Record W2551705398 · doi:10.2218/jls.v3i1.1454

A techno-typological analysis of fan (tabular) scrapers from Ein Zippori, Israel

2016· article· en· W2551705398 on OpenAlexaff
Katia Zutovski, Richard W. Yerkes, Aviad Agam, Lucy Wilson, Nimrod Getzov, Ianir Milevski, Avi Gopher

Bibliographic record

VenueJournal of Lithic Studies · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsScraper siteWadiArchaeologyChalcolithicBronzeSouthern LevantBronze AgeGeographyExcavationEngineering

Abstract

fetched live from OpenAlex

Fan (or tabular) scrapers are a diagnostic tool type in Chalcolithic Ghassulian and Early Bronze Age lithic assemblages from the southern Levant. To date, only small numbers of fan scrapers have been reported from the Late Pottery Neolithic Wadi Rabah culture. In this paper we present a techno-typological analysis of a fair sample of fan scrapers and fan scrapers spalls from Wadi Rabah and Early Bronze Age layers at Ein Zippori, Lower Galilee, Israel. Techno-typological similarities and differences of Wadi Rabah, Chalcolithic Ghassulian and Early Bronze Age fan scrapers from Ein Zippori and other sites in the region are presented, trends of change along time are noted, and an updated definition is proposed. Our results indicate that fan scrapers are highly efficient tools for accurate and prolonged animal butchering and hide working. The main advantage of fan scrapers is their mostly flat, thin morphology and large size that permits the creation of several relatively long working edges, various retouched angles (from sharp to abrupt), extensive resharpening, and a comfortable grasp. While fan scrapers were products of a local trajectory in Late Pottery Neolithic Wadi Rabah lithic industries at Ein Zippori, a standardized, off-site manufacturing of fan scrapers is evident during the Early Bronze Age.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.261
Teacher spread0.227 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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