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Record W3133967230 · doi:10.4000/techne.3542

La thérapéia des sculptures en Grèce ancienne : le témoignage des sources textuelles

2014· article· fr· W3133967230 on OpenAlexaff
Evridiki Leka

Bibliographic record

VenueSpotlight · 2014
Typearticle
Languagefr
FieldSocial Sciences
TopicClassical Antiquity Studies
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsArtSculptureHumanitiesArt history

Abstract

fetched live from OpenAlex

L’étude des textes littéraires et épigraphiques constitue le point de départ indispensable de l’enquête sur la restauration d’œuvres sculptées dans l’Antiquité. Quoique nous ne connaissions à ce jour aucun traité antique sur la conservation et la restauration en général, et encore moins concernant les sculptures, le bilan des informations, forcément ponctuelles et dispersées, voire parfois difficiles à interpréter, qui sont contenues dans les sources textuelles, nous conduit à penser que les Anciens se souciaient beaucoup d’entretenir et de restaurer les œuvres sculptées. Le caractère de leur démarche était cependant très différent du nôtre, comme l’attestent à la fois la nomenclature et les types des traitements, la nature des œuvres traitées, les circonstances et les lieux où les opérations avaient lieu, ainsi que l’identité des personnes qui en étaient responsables.

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)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.007
Science and technology studies0.0040.006
Scholarly communication0.0050.006
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0170.002

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 head0.015
GPT teacher head0.268
Teacher spread0.252 · 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 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

Citations6
Published2014
Admission routes1
Has abstractyes

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