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Record W4411455735 · doi:10.1038/s41597-025-05330-z

The Open Aurignacian Project: 3D scanning and the digital preservation of the Italian Paleolithic record

2025· article· en· W4411455735 on OpenAlexafffund
Armando Falcucci, Adriana Moroni, Fabio Negrino, Marco Peresani, Julien Riel‐Salvatore

Bibliographic record

VenueScientific Data · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsUniversité de Montréal
FundersUniversità degli Studi di FerraraEberhard Karls Universität TübingenEuropean CommissionSocial Sciences and Humanities Research Council of CanadaUniversité de MontréalUniversità degli Studi di SienaDeutsche ForschungsgemeinschaftUniversità degli Studi di GenovaMinistero della cultura
KeywordsAurignacianUpper PaleolithicArchaeology3d scanningMiddle Paleolithic3d modelGeographyFocus (optics)Computer scienceGeologyArtificial intelligence

Abstract

fetched live from OpenAlex

Here, we introduce an open-access database of 3D models of stone tools (n = 2,016) from four Early Upper Paleolithic sequences excavated south of the Alps and along Peninsular Italy, including Grotta della Cala, Grotta di Castelcivita, Grotta di Fumane, and Riparo Bombrini. Available through four self-standing Zenodo repositories, these models enable in-depth analysis of core reduction procedures, reduction intensity, and shape variability. Unlike other repositories, this database has been actively used to address archaeological questions, providing a comprehensive demonstration of the use of 3D models in lithic analysis. The Open Aurignacian Project utilizes various scanning devices, including the Artec Spider, Artec Micro, and micro-computed tomography, with a focus on enhancing the reproducibility and accessibility of archaeological data. This paper presents the scanning methodology, dataset organization, and technical validation of the project, while also discussing the scientific potential of these data to foster cross-continental research collaboration. Our open-sharing initiative is designed to stimulate inter-regional studies of human behavioral evolution, offering new opportunities to address questions in Paleolithic studies through the FAIR principles.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.005
Scholarly communication0.0010.001
Open science0.0040.003
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 head0.067
GPT teacher head0.353
Teacher spread0.286 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations3
Published2025
Admission routes2
Has abstractyes

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