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On the Names of Late Byzantine and Post-byzantine Cherson

2023· article· en· W4390346264 on OpenAlexaboutno aff
Никита Храпунов

Bibliographic record

VenueMaterials in Archaeology History and Ethnography of Tauria · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEurasian Exchange Networks
Canadian institutionsnot available
Fundersnot available
KeywordsByzantine architectureFifteenthAncient historyToponymyHistoryPeninsulaEmpireCONQUESTPeriod (music)Middle AgesQuarter (Canadian coin)ClassicsArchaeologyGeographyArt

Abstract

fetched live from OpenAlex

Byzantine Cherson, the Empire’s main outpost in the northern Black Sea area, was located in the south-western tip of the Crimean Peninsula. This article addresses new names of this city and, later, its ruins, which appeared from the fourteenth to eighteenth century. According to the written sources, the final decline and depopulation of this city dates to the middle or the third quarter of the fifteenth century. However, before that the Turks who lived in the vicinity called the city Sary-Kermen, or “Yellow fort.” From them, this place-name came first to Arabic writers and then to Western European travellers and cartographers. Later on, new names of the city appeared. In the seventeenth century, the sources documented the place-name Tope-Tarkan, or “Prince’s hill,” and in the eighteenth century, Church, or corrupted Χερσών. Obviously, these toponyms were in use simultaneously. In this period, Christian writers continued to call the city Cherson, but often forget the exact place where it stood. This situation reflected the changes in ethnic and language environment in the Taurica resulted by the thirteenth-century Mongol invasion and fifteenth-century Ottoman conquest, as well as the fact that the local residents forget the history of Cherson after a time its last residents left the city. Legends developed around the abandoned place, and the old ruins on the opposite (eastern) side of present-day Karantinnaia bay were probably called the city of Salunia.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
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 head0.031
GPT teacher head0.275
Teacher spread0.244 · 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
Published2023
Admission routes1
Has abstractyes

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