Bibliographic record
Abstract
Геополитическая обстановка в мире и отказ от долларовой системы взаиморасчетов между странами, привел к необходимости иметь большой золотой запас. По итогам 2024 года по объемам золотодобычи Россия занимает 2-ое место (310 т) после Китая (380 т), далее располагаются Австралия (290), Канада (200), США (160). Среди топ 20 компаний по добыче золота в 2022 году наши компании занимают следующие места: 6 -е место - Polуus Gold International (79 т), 15-е место Polymetal International (45.1 т), 18-е место Nord Gold (31.9 т). Для сравнения, 1ое место в списке занимает компания Newmont с объемом добычи 186,6 т. В России в лидерах 2022 года с большим отрывом от остальных регионов, остаются Красноярский край и Магаданская область, но их начинает догонять по объемам производства Якутия. Перспективы заметного прироста в ближайшие годы сохраняются у Забайкалья, Чукотки и Хабаровского края. Десятка крупнейших регионов-золотодобытчиков обеспечивает более 90% производства золота в России. The global geopolitical situation and the abandonment of the dollar system of mutual settlements between countries have led to the need for a large gold reserve. Based on the results of 2024, Russia ranks 2nd in gold production volumes (310 t) after China (380 t), followed by Australia (290), Canada (200), and the USA (160). Among the top 20 gold mining companies in 2022, our companies occupy the following places: 6th place Polyus Gold International (79 t), 15th place Polymetal International (45.1 t), 18th place Nord Gold (31.9 t). For comparison, 1st place on the list is occupied by Newmont with a production volume of 186.6 t. In Russia, the leaders in 2022 by a wide margin remain the Krasnoyarsk Territory and Magadan Oblast, but Yakutia is beginning to catch up in production volumes. Transbaikal, Chukotka, and Khabarovsk Krai remain poised for significant growth in the coming years. The top ten gold-producing regions account for over 90% of Russia's gold production.
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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 | 0.002 | 0.005 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".