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Record W4400732707 · doi:10.1080/00934690.2024.2369826

Chiaroscuro Photogrammetry: Revolutionizing 3D Modeling in Low Light Conditions for Archaeological Sites

2024· article· en· W4400732707 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Field Archaeology · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPhotogrammetryArchaeologyGeographyGeologyRemote sensing

Abstract

fetched live from OpenAlex

Archaeologists working in low light conditions have had difficulty producing 3D models that are both scientific and aesthetic. We are presenting chiaroscuro photogrammetry, a technique inspired by Renaissance artists, to solve this problem. The method is portable, inexpensive, low impact, adaptable, fast, and requires no additional expertise beyond photogrammetry. While first trialed on a rock and a tree that produced promising outcomes, the true test was on a panel of finger flutings in a completely dark chamber of Koonalda Cave, South Australia. The result was a 3D model of the finger flutings with evenly balanced light and deep colors, and the geometry of the model was free from holes and visible artifacts. The 3D model produced using chiaroscuro photogrammetry was visually and geometrically accurate, even more so than flash photogrammetry. Chiaroscuro photogrammetry has the potential to revolutionize 3D modeling in low light conditions for a variety of archaeological contexts.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.580

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)0.0010.000

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 head0.037
GPT teacher head0.268
Teacher spread0.231 · 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