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Record W7026827204

ASSESSMENT OF SHRINE RUINS AND MONUMENTAL TREES OF TLOS ANTIC CITY IN TERMS OF CULTURAL LANDSCAPE

2013· article· en· W7026827204 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicCultural and Sociopolitical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHuman settlementScope (computer science)Quarter (Canadian coin)CultLandscape designLandscape archaeologyCultural landscapeMetropolitan area
DOInot available

Abstract

fetched live from OpenAlex

Religious rituals such as the tradition of visiting shrines and tree cult have been in existence for thousands of years in the different regions of Anatolia. Clear examples of such still- existing rituals are the visits paid to “Türbe” Quarter in Yaka Village of Fethiye District in the Province of Muğla and making wishes and hoping for heals from the holly tree. The data, which has been obtained from the research that has been launched the scope of a TÜBİTAK (The Scientific and Technological Research Council of Turkey) Project in 2011 and the study that have been on the drawing of historical ruins and documentations in Türbe Quarter, have been transferred to the Geographic Information Systems. Although the certain date of the historical ruins is not exactly known, it has been thought that the ruins may belong to Menteşoğulları Seigniory of Türkmen Tribes. In the scope of project, the study have also been done to determine the age of 3 Cupressus sempervirens var. horizantalis (cypress) that are within the ancient ruins and their environs. The oldest one has been understood to be about 910 years old. As a result, in this study, historical ruins and monumental trees within the boundaries of Tlos Antic City being one of the important settlements in the Lycian Region have been assessed in termsof cultural landscape.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.277
GPT teacher head0.524
Teacher spread0.247 · 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
Published2013
Admission routes1
Has abstractyes

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