MétaCan
Menu
Back to cohort
Record W288137478 · doi:10.4000/archeosciences.2494

Observations and questions on gold artefacts from underwater excavations

2009· article· en· W288137478 on OpenAlexaff
Olivier Berger

Bibliographic record

VenueArchéoSciences · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicMaritime and Coastal Archaeology
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsExcavationByzantine architectureSettlement (finance)UnderwaterArchaeologyPeriod (music)HistoryUnderwater archaeologyComputer scienceVisual artsArtAestheticsWorld Wide Web

Abstract

fetched live from OpenAlex

In the present paper, we will study examples of settlement excavations that can sometimes reveal abundant gold material. These -underwater excavations took place in Alexandria and Aboukir Bay, Egypt, where we found numerous gold or gold-plated objects. Of these, some objects provide us with information about the trade routes used in those times, and others show us that refinement existed even during the Byzantine period. Some objects tell us fabulous stories, while others attest a high mastery of the available technology.Through specific examples, we will approach the scientific, aesthetic, historical and technological aspects pertaining to these gold objects.While most of these gold objects, jewellery and coins, have been studied before, and the results published elsewhere, considering the number of excavated objects, it is typically the less spectacular ones or the ones that were too difficult to interpret that have been neglected.Being an archaeologist specialised in the restoration of metal, I am thus not a metallurgist, a technologist, or even a numismatist, nor a historian specialised in the production or trade of gold objects. However, on the boat, underwater, or in the land laboratory, many questions concerning these archaeological objects deserve attention.With the help of three Case studies, we will see that some problems related to these objects have come up, and that a close collaboration with various gold artefact specialists appears necessary in order to try to solve these uncertainties.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.008
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.239
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueArchéoSciencesSame topicMaritime and Coastal ArchaeologyFrench-language works237,207