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Record W4293173469 · doi:10.51270/46.1.1

Using retroReveal as a Complement to DStretch for Enhancing Red Ochre Pictographs

2022· article· en· W4293173469 on OpenAlexvenueaboutno aff
T. Daniel Andrews, Jack W. Brink

Bibliographic record

VenueCanadian Journal of Archaeology · 2022
Typearticle
Languageen
FieldComputer Science
TopicImage Retrieval and Classification Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsRock artVisibilityComplement (music)Computer scienceGeologyArchaeologyArtVisual artsComputer graphics (images)HistoryChemistryPhysicsOptics

Abstract

fetched live from OpenAlex

The web-based program retro­Reveal has been used primarily for providing improved visibility of documents with faint text, including stamps, currency, music, and so forth. It has yet to be used to its full potential by archaeologists interested in rock art. The plugin DStretch, used on the ImageJ platform, has been the standard for enhancement of faint red ochre rock art images. We introduce retroReveal as a supplement to photographic investigation through comparison of images from four rock art sites in Alberta, Canada. Processing photographs with the two techniques typically yields comparable results, but often with slight differences. In a few cases, retroReveal makes certain features more apparent than is the case with DStretch; in other instances, the opposite is true. Other positive and negative aspects of the two techniques are discussed. Experiments with black pictographs indicate that retroReveal does not perform satisfactorily with these images. Overall, our results indicate that retroReveal should be added to the toolkit for illuminating painted rock art images.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.046
GPT teacher head0.308
Teacher spread0.261 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations2
Published2022
Admission routes2
Has abstractyes

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