MétaCan
Menu
Back to cohort
Record W4385841092 · doi:10.15184/aqy.2023.95

LiDAR and conflict archaeology: the Battle of the Bulge (1944–1945)

2023· article· en· W4385841092 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAntiquity · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeological Research and Protection
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBattleConflict archaeologyArchaeologyLidarHistoryKey (lock)Aerial surveyGeographyRemote sensingPrehistoric archaeologyComputer science

Abstract

fetched live from OpenAlex

Although conflict archaeology is now well established, the archaeological remains of many specific military confrontations are still to be explored. This article reports the results of fieldwork to document the site of the Battle of the Bulge (16 December 1944–25 January 1945). The authors use drone-mounted 1m-resolution LiDAR and very high-resolution simultaneous localisation and mapping (SLAM) methods to reveal more than 940 features within the forested Ardennes landscape, many of which were subsequently visited and confirmed. As well as highlighting the potential of the LiDAR-SLAM method, deployed here (both in this geographic region and in conflict archaeology) for the first time, the survey results emphasise the need for a debate on managing the heritage of a key modern conflict landscape in Europe.

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.

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)0.001
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 score0.107
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.042
GPT teacher head0.276
Teacher spread0.234 · 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