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Record W4324380800 · doi:10.1080/14614103.2023.2187520

The Midlands of England: Economic Backwater or an Agricultural Powerhouse? Environmental Evidence from Prehistory to Modern Times Recorded in Sediments from Aqualate Mere, Central England, UK

2023· article· en· W4324380800 on OpenAlexaff
Tim Mighall, N. J. Pittam, Ian Foster, Paul M. Ledger, Jason Jordan, Antonio Martı́nez Cortizas, Mark Bateman

Bibliographic record

VenueEnvironmental Archaeology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMemorial University of Newfoundland
FundersCoventry UniversityUniversity of Aberdeen
KeywordsPrehistoryWoodlandArchaeologyBronze AgeGeographyPopulationSedimentAgricultureErosionPeriod (music)GeologyEcologyPaleontology

Abstract

fetched live from OpenAlex

Archaeological and palaeoecological evidence relating to human activity in the English Midlands is scant compared to elsewhere in Britain. Knowledge of human activity in pre-Roman and Roman times is often fragmentary and disparate in parts of the region where it could be assumed that the resident population was small with little Roman impact. To examine these contentions, a palaeoenvironmental investigation from Aqualate Mere near Newport, Staffordshire, has been undertaken on the sediment record extending back to c. 1300 cal. BC. An analysis of microfossils, microscopic charcoal, sediment chemistry and mineral magnetism from a core dated by 14C, SCPs, 210Pb and 137Cs has provided an opportunity to reconstruct land use changes and atmospheric pollution from the later prehistoric period onwards. The results challenge the idea this region was a backwater as there is near-continuous agricultural activity around the mere since the Late Bronze Age through to modern times. This is characterised by phases of woodland decline, an intensification of farming, soil erosion, evidence for possible eutrophication and regional lead pollution.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.999

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.015
GPT teacher head0.217
Teacher spread0.203 · 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; both teacher heads agree on what is shown here.

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
Published2023
Admission routes1
Has abstractyes

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