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Record W2807356405 · doi:10.1073/pnas.1719420115

Fluctuating radiocarbon offsets observed in the southern Levant and implications for archaeological chronology debates

2018· article· en· W2807356405 on OpenAlexfundno aff
Sturt W. Manning, Carol B. Griggs, Brita Lorentzen, Christopher Bronk Ramsey, David Chivall, A. J. T. Jull, Todd Lange

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaGovernment of CanadaNational Science Foundation
KeywordsRadiocarbon datingChronologyArchaeologySouthern LevantGeologyOffset (computer science)Physical geographyGeographySouthern HemispherePaleontologyBronze AgeClimatology

Abstract

fetched live from OpenAlex

Significance We observe a substantive and fluctuating offset in measured radiocarbon ages between plant material growing in the southern Levant versus the standard Northern Hemisphere radiocarbon calibration dataset derived from trees growing in central and northern Europe and North America. This likely relates to differences in growing seasons with a climate imprint. This finding is significant for, and affects, any radiocarbon application in the southern Levant region and especially for high-resolution archaeological dating—the focus of much recent work and scholarly debate, especially surrounding the timeframe of the earlier Iron Age (earlier Biblical period). Our findings change the basis of this debate; our data point to lower (more recent) ages by variously a few years to several decades.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.068
GPT teacher head0.280
Teacher spread0.213 · 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

Citations49
Published2018
Admission routes1
Has abstractyes

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