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КАРТОГРАФИЧЕСКИЙ МЕТОД В ИЗУЧЕНИИ ПОМЕЩИЧЬИХ ВЛАДЕНИЙ ПСКОВСКОЙ ГУБЕРНИИ В ПОСЛЕДНЕЙ ЧЕТВЕРТИ XVIII В.

2018· article· en· W3212517299 on OpenAlexaboutno aff
Е. В. Гончарова

Bibliographic record

VenueАрхеология и история Пскова и Псковской земли · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsLandlordQuarter (Canadian coin)GeographyDistribution (mathematics)State (computer science)ArchaeologyCartographyHistoryLawPolitical scienceComputer science

Abstract

fetched live from OpenAlex

The study of the possessions of Pskov landowners requires cartographic material involvement but there are still no cartographic constructions on the basis of which it would be possible to present the spatial distribution of landlords ‘ villages in different districts of the Pskov province in the 18 century. “The Economic notes to the General surveying” and related cartographic material are considered by historians the most reliable source for this purpose. For spatial distribution of nobles’ estates on the territory of the Pskov province the data of the geometrical map made according to the General land surveying in 80 - 90ies of the 18 century from the Russian state Archive of ancient acts was involved.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.006

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 head0.033
GPT teacher head0.350
Teacher spread0.317 · 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 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
Published2018
Admission routes1
Has abstractyes

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