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Record W3150988825 · doi:10.1371/journal.pone.0249606

Where myth and archaeology meet: Discovering the Gorgon Medusa’s Lair

2021· article· en· W3150988825 on OpenAlexaff
Clive Finlayson, José María Gutiérrez López, María Cristina Reinoso del Río, Antonio M. Sáez Romero, Francisco Giles Guzmán, Geraldine Finlayson, Francisco Giles Pacheco, David Abulafia, Stewart Finlayson, Richard P. Jennings, Joaquı́n Rodrı́guez Vidal

Bibliographic record

VenuePLoS ONE · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicMaritime and Coastal Archaeology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsContext (archaeology)CaveArchaeologyMythologySituatedGeographyCartographyArtClassicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Here we report the discovery of ceramic fragments that form part of a Gorgoneion, a ceramic image representation of the Gorgon Medusa. The fragments were found in a deep part of Gorham's Cave, well known to ancient mariners as a natural shrine, between the 8th and 2nd century BCE. We discuss the context of this discovery, both within the inner topography of the cave itself, and also the broader geographical context. The discovery is situated at the extreme western end of the Mediterranean Sea, where it meets the Atlantic Ocean. The location was known to ancient mariners as the northern Pillar of Herakles, which marked the end of the known world. We relate the discovery, and its geographical and chronological context, to Greek legends that situated the lair of the Gorgon sisters at a location which coincides with the physical attributes and geographical position of Gorham's Cave. We thus provide, uniquely, a geographical and archaeological context to the myth of Perseus and the slaying of the Gorgon Medusa.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.009
Scholarly communication0.0030.003
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.193
Teacher spread0.156 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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Same venuePLoS ONESame topicMaritime and Coastal ArchaeologyFrench-language works237,207