100th anniversary of the discovery of the Verkhnekamskoye deposit of potassium and magnesium salt
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The richest natural heritage of the Perm Krai is connected with the ancient Perm Sea. Its drying led to the formation of lagoons, which were eventually covered by sedimentary rocks. As a result, the Verkhnekamskoye deposit of potash and magnesium salts (VDPMS), the largest in the world in terms of ore reserves (after Canada), was formed in the north of the region. This made it possible for the Northern Kama Region to become the largest supplier of table salt and soda for the regions of Russia during the pre–Soviet period, and during the Soviet period it became the most important and practically the only center for the production of potash fertilizers and non-ferrous metals, primarily magnesium and titanium. Based on materials from the State Archive of the Perm Krai (SAPK), the Perm State Archive of Socio-Political History (PermSASPH), the collections of the Solikamsk Museum of Local Lore, the Bereznikov Historical and Art Museum named after I. F Konovalov, corporate museums of Berezniki potash workers, metallurgists and nitrogen workers the contribution of scientists whose research helped Professor Pavel Ivanovich Preobrazhensky discover the world’s richest deposit of potash and magnesium salts in Verkhnekamy on October 5, 1925, is shown. That contributed not only to the rapid industrial and socio-cultural development of the Western Urals, but also ensured the steady growth of industrial potential in many sectors of the national economy, significantly increased the country’s defense capability during the Soviet and post-Soviet periods, and became Russia’s only center for titanium-magnesium production and rare earth elements.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it