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The State of Preservation of the Shakhty Rock Art Site and the Prospects of Its Conservation

2022· article· en· W4303044646 on OpenAlexaboutno aff
И. В. Аболонкова, Nuritdin Sayfulloev, I. Е. Dedov

Bibliographic record

VenueArchaeology Ethnology and Anthropology of Eurasia · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
Fundersnot available
KeywordsRock artArchaeologyConservationNature ConservationState (computer science)GeographyQuarter (Canadian coin)Environmental planning

Abstract

fetched live from OpenAlex

This article deals with the preservation of the Shakhty rock art site, discovered in the Eastern Pamirs in 1958 by the leading Central Asian Stone Age researcher V.A. Ranov. The analysis of photographs taken in the Shakhty rock shelter during the 2019 survey revealed the nature of destructive processes at the site due to environmental conditions of the Eastern Pamir highland. The article integrates the results of analysis of Ranov’s archives at the Donish Institute of History, Archaeology and Ethnography of the National Academy of Sciences, Republic of Tajikistan. Thanks to Ranov’s diaries and photographs, it was possible in 2019 to assess the degree of erosion on the rock surface, and the loss of fragments of painted images over more than 60 years. Emergency areas requiring conservation efforts were identified. Principles of conservation and restoration of rock art are outlined, and an overview of techniques developed for sites of this type in the post-Soviet space in the last quarter of the 20th century is presented. State of the art conservation methods for rock art, which, in the future, can be applied for the preservation of emergency areas at Shakhty, are described. A set of measures is suggested to preserve this site.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.021
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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 teacher head, not a consensus.

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
Published2022
Admission routes1
Has abstractyes

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