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Record W4410502896 · doi:10.1558/jma.33428

Early Bronze Age Stone Tools and Agricultural Innovations

2025· article· en· W4410502896 on OpenAlexaff
Jacques Chabot, Richard W. Yerkes

Bibliographic record

VenueJournal of Mediterranean Archaeology · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBronze AgeBronzeArchaeologyAgricultureAncient historyGeographyHistory

Abstract

fetched live from OpenAlex

The intensification of agricultural production in the Fertile Cresent during the Early Bronze Age (EBA) is reflected in two farming tools: Canaanean blades, which were thinner than Neolithic blades; and the threshing sledge, into which some of these blades were inserted. In northern Mesopotamia, most Canaanean blades were long and wide, made by pressure using a lever and copper point, although some were narrower and produced by indirect percussion or by pressure with a crutch. In the Levant, however, although some wide examples have been recovered, narrow forms predominated. Controlled experiments have revealed that microwear on blade segments used in sledge inserts is different from microwear on segments used as sickles. This paper analyses samples of Canaanean blades and blade segments from Tell ʿAtij in northern Mesopotamia and Ein Zippori in the Levant to examine (1) whether wider and narrower Canaanean blade segments were used for different tasks, (2) whether some Canaanean blade segments were designed specifically to be inserted into sickle handles and used for harvesting and (3) whether sickle inserts were recycled and used in threshing sledges. We also discuss how production and use of Canaanean blades is related to EBA agricultural intensification.

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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.020
GPT teacher head0.233
Teacher spread0.212 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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