Uranium mines and the biosphere: from oppression to the biota mutagenesis
Bibliographic record
Abstract
В статье представлена история урановых рудников с давних пор: детализированы природно-техногенные объекты Кан-и-Гут (Рудник погибели) и рудник Туя-Муюн, а также другие рудники Ферганской долины. Показано, что уранодобывающие предприятия характерны для многих стран мира: Австралии, Казахстана, России, Канады, ЮАР, Украины, Узбекистана, США, Бразилии и Намибии. Показаны основные рудные урановые минералы. Рассмотрены способы добычи и переработки урансодержащих пород. Раскрыты главные аспекты воздействия урановых рудников на геосферу Земли (в том числе и на биосферу). Выделен период полураспада урана и отдельно показаны виды радиационного воздействия на геосферу от урановых рудников. Объяснены процессы угнетения биоты радиоактивным излучением, а также появление мутагенеза, вплоть до окультуренной растительности. The article presents the history of uranium mines since ancient times: the natural and man-made objects Kan-i-Gut (Perdition Mine) and Tuya-Muyun mine, as well as other mines of the Ferghana Valley, are detailed. It is shown that uranium mining enterprises are typical for many countries of the world: Australia, Kazakhstan, Russia, Canada, South Africa, Ukraine, Uzbekistan, the United States, Brazil and Namibia. The main ore uranium minerals are shown. Methods of extraction and processing of uranium-containing rocks are considered. The main aspects of the impact of uranium mines on the Earth's geosphere (including the biosphere) are revealed. The half-life of uranium is distinguished and the types of radiation effects on the geosphere from uranium mines are shown separately. The processes of biota suppression by radioactive radiation, as well as the appearance of mutagenesis, up to cultivated vegetation, are explained.
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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 | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".