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Record W2729821781 · doi:10.3138/mous.14.2-2

The North Field Excavations at Vindolanda: Preliminary Report on the 2009–14 Exploratory Field Seasons

2017· article· en· W2729821781 on OpenAlexaffvenueabout
Elizabeth M. Greene, Alexander Meyer

Bibliographic record

VenueMouseion Journal of the Classical Association of Canada · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicArchaeology and Historical Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsExcavationArchaeologyDitchFrontierPeriod (music)ThrivingSettlement (finance)Quarter (Canadian coin)GeographyHistorySociologyArt

Abstract

fetched live from OpenAlex

Since 2009, work has been carried out in the North Field at Vindolanda, a Roman military fort in the frontier zone of Roman Britain. The excavations have been directed by the Vindolanda Trust, in conjunction with a team from the University of Western Ontario. Based on these first seasons, some significant preliminary conclusions about this area of the site can be made, which will be important in moving forward with further excavations and for future interpretations of the early occupation of this region by the Roman army. The North Field appears to have been occupied already in the last quarter of the first century and may prove with further excavation to contain a fort that pre-dates “Period I” at Vindolanda, settled in ad 85. The field also appears to have been an area of significant activity in the second and third centuries. Large stone structures and a fortified ditch enclosed at least part of the space in this period. These buildings were part of the thriving third-century extramural settlement explored extensively elsewhere at Vindolanda over the past decades. The findings from these five seasons (2009–2010 and 2012–2014) have led to further excavation campaigns in the North Field, which will continue for several more field seasons. This article disseminates the results of the exploratory trenches as the research moves forward with further excavation.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.214
Teacher spread0.192 · 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.

Study designNot applicable
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

Citations3
Published2017
Admission routes3
Has abstractyes

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