IMPROVING THE BARREL IRON PRODUCTION IN THE URAL MINING PLANTS IN THE SECOND QUARTER OF THE 19TH CENTURY
Bibliographic record
Abstract
The article, based on materials from federal and regional archives, describes the problems in the production of iron for gun barrels in the Ural mining plants in the second quarter of the 19th century. It is noted that the mining industry enterprises have been supplying the weapons factories with metal since the 18th century. Despite the approval in the early 19th century of regulatory acts defining the requirements for the properties and dimensions of iron, and quality control by representatives of the military department, no special rules for its acceptance have been developed. The lack of clear verification requirements led to the fact that weapons factories received a significant amount of iron of unsatisfactory quality, unsuitable for gunsmiths to weld barrels. As a result, the amount of defective metal increased, and the debt of mining plants to the military department grew. The mining and military departments began to pay attention to the problem of improving the barrel iron production in the 1820s. Special committees of representatives from both departments developed rules for testing iron, but they were not included in the new instructions for accepting military products from mining plants in 1831. Their development dragged on until the mid-1840s. Improvements in the technology for barrel iron production were made through experiments conducted by both the military department and the Ural mining plants. As a result, during the period under study, it was not possible to obtain iron suitable for weapons factories.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".