MétaCan
Menu
Back to cohort
Record W2096723932 · doi:10.1260/1478-0771.5.2.200

Detailed 3D Modelling of Castles

2007· article· en· W2096723932 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

VenueInternational Journal of Architectural Computing · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsNational Research Council Canada
FundersProvincia Autonoma di Trento
KeywordsWorkflowPhotogrammetryComputer scienceAutomationTask (project management)VisualizationComputer graphics (images)Engineering drawingData miningArtificial intelligenceEngineeringSystems engineeringDatabase

Abstract

fetched live from OpenAlex

Digitally documenting complex heritage sites such as castles is a desirable yet difficult task with no established framework. Although 3D digitizing and modelling with laser scanners, Photogrammetry, and computer aided architectural design (CAAD) are maturing, each alone is inadequate to model an entire castle in details. We present a sequential approach that combines multiple techniques, each where best suited, to capture and model the fine geometric detail of castles. We provide new contributions in several areas: an effective workflow for castle 3D modelling, increasing the level of automation and the seamless integration of models created independently from different data sets. We tested the approach on various castles in Northern Italy and the results demonstrated that it is effective, accurate, and creates highly detailed models suitable for interactive visualization. It is also equally applicable to other types of large complex architectures.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.036
GPT teacher head0.258
Teacher spread0.222 · 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