Mitigation of geoecological impact of underground potash mining
Bibliographic record
Abstract
Mineral deposits holding the largest reserves of potassium are the Upper Kama Potash–Magnesium Salt Deposit (Russian Federation), Starobinsk Potash Salt Deposit (Belarus), Saskatchewan Salt (Canada) and potash salt deposits in Germany. The CIS countries manufacture potash fertilizers mainly at the largest deposits of potash salts (Starobinsk, Belaruskali) and potash–magnesium salts (Upper Kama, Uralkali) that are extracted with the underground method. Potassium chloride content of produced ore varies between 24 and 32%. In sylvinite ore dressing, waste make 65–75% where 92–95% of solid is sodium chloride and liquid waste is clayey–salt slurry. Waste amount to 2.3–5.7 g per 1 g of the main product. In future it is possible to enhance potash fertilizer production by means of taking assured reserves of Gremyachinsk, Nepsk, Eltonskoe deposits in Russia, Garlyk in Turkmenistan and Petrikovsk deposit in Belarus. Potash mining induces adverse, sometimes large-scale or disastrous change of geoecological situation in industrial areas. The authors discuss issues connected with mitigation of geoecological impact of underground potassium salt mining. The article reports the related research findings and offers practical solutions in the areas of minimization of loss in potash mining, potash mine flooding countermeasures, as well as technologies for halite and slurry waste storage in terms of the ground and mine-technical conditions of Starobinsk potash salt deposit.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".