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Record W7082035122 · doi:10.17580/gzh.2025.07.16

Production dynamics at government-owned mining works in Russia in Nicholas I period

2025· article· en· W7082035122 on OpenAlexaboutno aff

Bibliographic record

VenueGornyi Zhurnal · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Production (economics)Competition (biology)ReignPeriod (music)Precious metalPig ironMining industry

Abstract

fetched live from OpenAlex

The article reviews development of production at the government-owned mining works in Russia in the second quarter of the 19th century. The source was the historical data of the Most Loyal Reports of the Department of Mining and Salt to the Emperor. The statistics of mineral mining, metallurgy and metal working, as well as shipment of products at the government-owned works in the Ural is analyzed. In the period of reign of Nicholas I, production at the government-owned mining works never grew (gold production was at a level of 135 poods (pood is a traditional Russian unit of weight, equal to 16 kilos), iron ore—3.9 million poods, copper ore–round 1.2 million poods with a decreasing trend, black coal—490 thousand poods, copper smeltery produced round 30.5 thousand poods, cast iron supply reduced from 1.5 million to 750 thousand poods, iron supply grew from 490 to 650 thousand poods, manufacturing of cannons was round 58 thousand poods, shells—round 204 thousand poods, anchors—12 thousand poods, cold guns—35 thousand items, scythes—12 thousand items). The zero growth was conditioned by the objectives set by the law for the government-owned mining industry. The increase in production could either lead to a competition with private factories, which was legally prohibited, or to an increase of budget expenses. Furthermore, the research revealed the specifics of the inventory adopted by the mining industry: the ‘reports’ informed not on the amount of metal produced but on the amount of metal supplied to customers and for market sales.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.213
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.

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
Published2025
Admission routes1
Has abstractyes

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