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Record W4409966126 · doi:10.1007/s00334-025-01045-8

Cereal crops in Yorkshire, UK (4000 bce–1100 ce)

2025· article· en· W4409966126 on OpenAlexfundno aff
Neal Payne

Bibliographic record

VenueVegetation History and Archaeobotany · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research CouncilDepartment of Anglo-Saxon, Norse and Celtic, Faculty of English, University of Cambridge
KeywordsBiogeosciencesAgronomyGeographyArchaeologyGeologyBiologyEarth science

Abstract

fetched live from OpenAlex

Abstract The substantial corpus of unpublished commercial and research excavation reports produced in the United Kingdom provides valuable data for investigating macro-scale changes in the archaeobotanical record. This article presents a comprehensive synthesis and reanalysis of archaeobotanical evidence from Yorkshire spanning the first evidence of cultivated cereal crops from ca. 4000 bce until 1100 ce . Yorkshire’s macro-botanical evidence has been collated and analysed using ubiquity and relative abundance data to establish long-term diachronic trends in the regional cereal crop records. Radiocarbon dated cereals have also been assembled to characterise the chronology of introductions and shifts in crop choice. The main outcomes of this analysis are: (1) a refinement of the chronologies for the introduction of new cereals to Yorkshire and (2) a clarification of their long-term trajectories as crops in the region. This article demonstrates that Roman Period arable practices were firmly rooted in pre-existing Iron Age traditions, with little alteration following conquest. Results also show a significant transition in the post-Roman Period away from a spelt wheat agriculture to barley agriculture complemented by other emerging free-threshing cereals. The climatic and socio-cultural context of this transition in the 5 and 6th centuries ce is discussed.

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)0.000
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 score0.062
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.009
GPT teacher head0.193
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 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

Citations5
Published2025
Admission routes1
Has abstractyes

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