Experience in Reconstruction of Agricultural Land Use for Balakhna District of Nizhniy Novgorod Gubernia in the 18th–19th Centuries (on the Basis of Cartographic Sources)
Bibliographic record
Abstract
Land use and soil cover patterns of arable lands in Balakhna district of Nizhny Novgorod gubernia in the last quarter of the 18th century and in the middle of the 19th century were studied with the use of the General Land Survey plan of Balakhna district (scale 1 : 84000), the map of Nizhny Novgorod gubernia by Mende (scale 1 : 42000), and the State Soil Map (scale 1 : 1 M). The data obtained attested to a steady and considerable decrease in the plowed area during that period. According to the historical materials, at the end of the 18th century, plowland occupied about 1290 km 2 , or 32% of the entire district (4200 km 2 ). In the middle of the 19th century, the plowed area decreased to about 990 km 2 (25%). According to the modern statistical data on land use in Balakhna district (within its boundaries of the 18th–19th centuries), the area of plowed fields is less than 700 km 2 (18%). This means that at least 14% of the study area is occupied by the postagrogenic soils. If we take the plowed area of the district in the 18th century for 100%, we can conclude that more than 40% of formerly plowed lands have been transformed into long-term fallows. The absolute predomination of soddy-podzolic soils (Retisols) is typical of the soil cover of Balakhna district. In the course of the reduction of the plowland area from the end of the 18th century to the middle of the 19th century, the percent of different soils composing this area did not change much. In general, the impact of soil quality on the decrease in the plowland area in that period is not observed.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".