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Record W2576101864 · doi:10.5281/zenodo.2552123

Integrating Heterogeneous Coin Datasets in the Context of Archaeological Research.

2015· article· en· W2576101864 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

VenueZenodo (CERN European Organization for Nuclear Research) · 2015
Typearticle
Languageen
FieldComputer Science
TopicImage Processing and 3D Reconstruction
Canadian institutionsPrairie Improvement Network
Fundersnot available
KeywordsContext (archaeology)Computer scienceArchaeologyData scienceGeography

Abstract

fetched live from OpenAlex

This paper describes the activities carried out under the ARIADNE project to demonstrate the item-level integration process of archaeological archives through the use of semantic technologies. To this end, some ancient coin records, coming from the archives of important European archaeological institutions, were selected. The subset thus created, has been carefully analysed by means of specific tools to identify similar concepts and common metadata elements that could serve as the basis for integration. CIDOC CRM was chosen as the conceptual model for encoding the identified entities, while some important numismatic vocabularies have been employed to improve standardisation. The implementation phase has benefited from the use of advanced tools for mapping and conversion of the original information in a semantic form (RDF), the creation of a triple store to place the newly integrated data and the necessary interfaces for accessing and querying them.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.086
GPT teacher head0.295
Teacher spread0.209 · 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