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Record W1600534686

Multi-Resolution Digital 3D Imaging System Applied to the Recording of Grotto Sites: the Case of the Grotta dei Cervi

2006· article· en· W1600534686 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsImage resolutionComputer scienceLaser scanningScannerResolution (logic)DocumentationDigital imagingObject (grammar)Remote sensingData setArtificial intelligenceGeologyComputer visionComputer graphics (images)Digital imageLaserOpticsImage (mathematics)Image processingPhysics
DOInot available

Abstract

fetched live from OpenAlex

The Grotta dei Cervi is a complex and fragile Neolithic cave where human presence left a large number of unique pictographs and petroglyphs. Detailed documentation necessitates recording it at different levels of details or spatial resolutions. A general approach would entail a combination of 3D data from different 3D sensors and information from different sources in order to meet set resolution targets. We used a prototype multi-resolution 3D laser imaging scanner that allowed acquiring the shape information of the three main chambers with a spatial resolution that improves with shorter standoffs. The system can record 3D data at a camera-to-object distance which ranges from 0.5 m to 10 m. At a standoff of 0.75 m, it provides a depth uncertainty of 0.08 mm and an optical lateral resolution of 0.2 mm on actual rock surfaces. This paper presents the project and the results obtained. The 10-day long visit into the Grotto generated more that 100 GB of 2D and 3D data that requires the development of new tools for modelling and managing the archive.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.634

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 studies0.0000.000
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 head0.016
GPT teacher head0.200
Teacher spread0.184 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations18
Published2006
Admission routes1
Has abstractyes

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