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Record W3128832788 · doi:10.3390/earth2010004

Using XVIII–XIX Cent. Maps and Modern Remote Sensing Data for Detecting the Changes in the Land Use at Bulgarian Fortified Settlements in the Volga Region

2021· article· en· W3128832788 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEarth · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsMemorial University of Newfoundland
FundersRussian Foundation for Basic Research
KeywordsBulgarianHuman settlementArable landGeographyLand useCultural heritageSatellite imageryRemote sensingAgricultureCartographyPhysical geographyArchaeologyCivil engineering

Abstract

fetched live from OpenAlex

This study uses modern and historic spatial data to analyze land use around 13th Century AD Bulgarian fortified settlements to examine the current state of these features and how changes in land use over the past two centuries have affected these features. Historic maps from the late 18th to the early 19th centuries AD were used alongside Landsat 8 images from 2015–2018 as a source of information about land use. Based on the results of archival map analysis and the classification of satellite imagery, the interpretation of land use around Bulgarian fortified settlements was possible. This method generated new data about land use dynamics near cultural heritage sites in the Volga Region. The diachronic study of sequential map data allowed researchers to further understand how anthropogenic factors have impacted the survivability of Bulgarian sites in the region. Among these, arable farming, hydro-electric power generation, and urban growth have had the greatest impact on these features.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.870

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 studies0.0010.000
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 head0.118
GPT teacher head0.260
Teacher spread0.142 · 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