Manufacturing of Military Products According to Naval Orders by Mining Plants of the Urals at the End of the 18th — First Quarter of the 19th Centuries
Bibliographic record
Abstract
The purpose of the research. The article discusses the organization of the implementation of orders of the maritime department by state-owned mining plants in the Urals. Based on materials from federal and regional archives, as well as regulations, the formation of a system for the supply of metals and other products for the fleet at the end of the 18th century is shown. According to the Manifesto of May 21, 1779, only state-owned mining plants were to carry out orders for the army and navy. At the beginning of the 19th century with the approval of instructions for the acceptance of guns, shells and metals from mining plants, a regulatory supply base for army and navy orders is formed. The author is noted that Ural enterprises supplied iron for the needs of shipbuilding and port construction, anchors and cast iron for ballast. In the first quarter of the 19th century the specialization of the region's mining plants in the production of products for the Baltic ports and the Arkhangelsk port took shape. Some of the metals and anchors were sent to the Black Sea ports. With the increase in military threat on the eve and during the Napoleonic wars and the growth of shipbuilding, the volume of orders of the naval department increased. As a result, already in the second decade of the 19th century mining factories could not fully carry out military orders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".