CENTENARY OF THE FIRST-EVER FIELD TRAINING FOR GEOGRAPHY STUDENTS OF THE MOSCOW UNIVERSITY IN THE MOSCOW REGION
Bibliographic record
Abstract
In 1922, a firstever stationary general geographical field training for students of the Moscow University was held in the Moscow region. The paper is inspired by a hundred-year-old publication by a trainee, then student B. Shustov, which describes the reasons for organizing the students’ fieldwork at the geographical station in the Vereya district of Moscow Governorate, the place and the format of training, and the research program including mainly meteorological, geomorphologic, geodetic and biogeographic components. Due to various circumstances, the first experience of stationary training in the Moscow region went to nowhere. Later, there were further attempts to arrange a permanent base for training, but they became successful only after almost a quarter of a century. At the same time, the first experience of such training formed some initial principles for conducting general geographical research for educational and practical purposes in the Moscow area. The article also provides information about the author of the publication, B.S. Shustov, who after graduating from the university was for a decade and a half actively engaged in scientific and teaching activities at the Research Institute of Geography and at the geographical department (later - the faculty) of the Moscow State University, and then in the Ryazan oblast.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".