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Record W4385663455 · doi:10.1017/rdc.2023.40

DATING ANCIENT CANAL SYSTEMS USING RADIOCARBON DATING AND ARCHAEOLOGICAL EVIDENCE AT TELLO/GIRSU, SOUTHERN MESOPOTAMIA, IRAQ

2023· article· en· W4385663455 on OpenAlexfundno aff
Ella Egberts, Jaafar Jotheri, Angelo Di Michele, Antony Baxter, Sébastien De Rey

Bibliographic record

VenueRadiocarbon · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
FundersQueen's UniversityQueen's University BelfastGetty Foundation
KeywordsMesopotamiaRadiocarbon datingArchaeologyCharcoalGeologyGeographyAncient historyHistory

Abstract

fetched live from OpenAlex

ABSTRACT Mesopotamia is often regarded the “cradle of civilization.” The development of water management practices in the region is thought to have played a key role in the emergence of these early civilizations. We present the first direct dating of a palaeo-canal system at the ancient city of Girsu, Mesopotamia (modern Iraq) (occupied between 4800 and 1600 BC). We describe the use of archaeological and radiocarbon (14C) dating techniques to establish the age of this canal system. Our results show considerable differences between shell 14C dates on the one hand and charcoal 14C dates and archaeological evidence on the other. This likely reflects the impact of freshwater reservoir effects from the Tigris and Euphrates Rivers. Although the FRE from rivers is widely acknowledged, its impact on 14C dates in Mesopotamia is rarely discussed and poorly understood. Our results provide a first indication of its variability and magnitude. With the publication of our results we aim to highlight the problem and re-initiate collaborative research efforts in improving 14C dating in this important region.

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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.054
GPT teacher head0.239
Teacher spread0.184 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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