ПРОБЛЕМЫ ПОСТАВОК МЕТАЛЛОВ ДЛЯ ОРУЖЕЙНЫХ ЗАВОДОВ КАЗЁННЫМИ ГОРНЫМИ ЗАВОДАМИ УРАЛА В КОНЦЕ XVIII - ПЕРВОЙ ЧЕТВЕРТИ XIX ВЕКА
Bibliographic record
Abstract
В статье показаны трудности в выполнении нарядов оружейных заводов казёнными горными заводами Урала в конце XVIII - первой четверти XIX вв. Источниками исследования послужили материалы федеральных и региональных архивов, описания оружейных предприятий, составленные артиллерийскими офицерами, а также нормативные акты. Отмечается, что горные заводы региона с XVIII в. снабжали металлом оружейные заводы, а в начале XIX в. они становятся основным поставщиком сырья для изготовления огнестрельного оружия. Показано, что значительное количество бракованного железа вызывало нарекания военного ведомства и привело к созданию специальных комитетов, которые изучили причины производства некачественного металла. Горное ведомство в ответ обвиняло в изготовлении бракованного оружия оружейных мастеров. Одной из причин поступления на оружейные заводы ствольного железа низкого качества были нечёткие требования к его приёмке, зафиксированные в инструкции 1804 г. В итоге, в изучаемый период так и не удалось получить железо нужного качества для заварки стволов. Горные заводы накапливали задолженности перед оружейными предприятиями, выполняя наряды не в полном объёме. The article shows the difficulties in the implementation of weapons factory orders by the state-owned mining plants of the Urals in the late 18 - first quarter of the 19 centuries. The sources of the study were materials from federal and regional archives, descriptions of weapons enterprises compiled by artillery officers, and regulatory acts. The author notes that the mining plants of the region supplied weapons factories with metal from the 18 century, and in the early 19 century they became the main supplier of raw materials for the manufacture of firearms. The author shows that a significant amount of defective iron caused complaints from the military department and led to the creation of special committees that studied the causes of low-quality metal production. The mining department, in turn, blamed weapons masters for the production of defective weapons. One of the reasons for the receipt of low-quality barrel iron at the weapons factories was the unclear requirements for its acceptance, recorded in the instructions of 1804. As a result, during the studied period, it was not possible to obtain iron of the required quality for welding barrels. Mining plants accumulated debts to weapons factories, not fulfilling orders in full.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.017 |
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".