Norilsk industrial ecological-geological system, its geocryological features, and anthropogenic impact on it
Bibliographic record
Abstract
Along with being one of the biggest northern industrial IEGSs globally, the Norilsk IEGS is also one of the most ecologically troublesome. One of the large-scale environmental disasters was the diesel fuel spill in Norilsk, which occurred on May 29, 2020, in the Kayerkan area of the city of Norilsk. The accident resulted in the leak of about 21,000 tons of diesel fuel. This disaster was one of the largest in the history of the Arctic: fuel entered the soil and nearby water bodies, including the Ambarnaya and Daldykan rivers, as well as Lake Pyasino, which is connected to the Kara Sea. The cause of the accident was the subsidence of the reservoir due to permafrost degradation, aggravated by the lack of timely repairs. The spill resulted in massive contamination of soil and aquatic ecosystems, destruction of fish populations, and accumulation of heavy metals in their livers. Norilsk Nickel is the largest industrial polluter in the Arctic. Norilsk Nickel enterprises annually emit about 1.7 million tons of harmful substances into the atmosphere, and the total volume of emissions in the entire Arctic zone of Canada in 2021 was 57 times less than Norilsk's annual emissions. In 2022, Norilsk accounted for 10.5% of all industrial emissions in Russia. Norilsk is located in a zone of continuous permafrost. In winter, the air temperature can drop to –60°C. Climate change and rising average annual temperatures in the Arctic lead to higher temperatures and even partial degradation of permafrost, which threatens urban infrastructure. Over the coming decades, a significant portion of the buildings in Norilsk will suffer due to foundation subsidence. By 2021, more than 40% of buildings in Norilsk had already been subjected to deformation. Palsa finds are known in the northernmost regions, including the vicinity of Norilsk and even in the northernmost regions of Taimyr. Technogenic seasonal injection frost blisters can form near Norilsk due to large water leaks. The paper summarizes the data characterizing the features of the technoedaphotope, technomicrocoenosis, technophytocoenosis, and technozoocenosis of the Norilsk industrial ecological-geological system and its technogenic transformation: a. The thickness of permafrost within the Taimyr engineering-geological megastructure is 600–1000 m. In most of the region, the average annual ground temperature varies from –11 to –13°C; b. The temperature regime of soils in the central part of Norilsk is characterized by positive dynamics of the average annual ground temperature from –7 (in the 1950s) to –3°C (in 2024); c. Palsa (heaving mounds of migration type) are found in the northernmost regions, including the vicinity of Norilsk and even in the northernmost regions of Taimyr; d. Large palsa are found on the southern edge of the Norilsk Plateau in the valley of the Turumakit River, as well as near the Dudinka town; e. Large syngenetic ice wedge ice was encountered in the high terrace of the Sabler Cape, in the basin of the Lake Labaz, near the Sopochnaya Karga Cape and near Dikson; f. The development of the edaphotope and its technogenic transformation within the Norilsk industrial EGS occurs under the active influence of cryogenesis. It was previously established that the distribution of heavy metals by the profile of contaminated soils changes depending on the source of pollution. In the case of predominance of aerotechnogenic input of pollutants, one clearly expressed maximum is observed in the surface organogenic horizon, and two maxima of accumulation of heavy metals in the soil profile are observed near waste disposal facilities; g. Technogenic transformation of the microbiocenosis of the Norilsk EGS is manifested in the low content of microbial biomass, due to the weakly developed vegetation cover in the city and the high level of heavy metals and other pollutants; h. Technogenic transformation of the zoocenosis has the greatest impact on the number of tundra populations of wild reindeer, which is declining due to overhunting, poaching and the growth of the wolf population, as well as the construction of linear structures and the extension of navigation periods on rivers; i. Technogenic transformation of the phytocenosis of the Norilsk EGS is explained by the poorly developed vegetation cover in the city, unfavorable physical and chemical properties of the soil and, above all, the high level of heavy metals and other pollutants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".