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Record W4393985200 · doi:10.1080/15740773.2024.2330916

Geophysical approaches to the archaeological prospection of early modern battlefield landscapes: a review of methods and objectives

2024· review· en· W4393985200 on OpenAlexfundno aff
Duncan Williams, Kate Welham, Stuart Eve, Philippe De Smedt

Bibliographic record

VenueJournal of Conflict Archaeology · 2024
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicArchaeological Research and Protection
Canadian institutionsnot available
FundersUniversity of GlasgowSocial Sciences and Humanities Research Council of CanadaBournemouth University
KeywordsProspectionBattlefieldArchaeologyHistoryGeographyAncient history

Abstract

fetched live from OpenAlex

This paper reviews methodological approaches in battlefield archaeology with a focus on sites of the early modern period, ca.17th-19th century.The challenges associated with the prospection of these sites partially explains the relative lack of serious research in this area until the late 20th century.While acknowledging the foundational role of conventional metal detection in overcoming these difficulties, it is argued that other less widely deployed geophysical methods should be increasingly used as part of an integrated approach to studying battlefield landscapes.Targets of interest are reviewed alongside the geophysical properties that might enable their detection and a selection of case studies successfully deploying these approaches within battlefield archaeology and adjacent disciplines are considered.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0100.010
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.181
GPT teacher head0.389
Teacher spread0.208 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2024
Admission routes1
Has abstractyes

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