Settlement System of the Kazan County In the Middle of the XIX Century
Bibliographic record
Abstract
The urgency of the problem under investigation is caused by the fact that the system of the settlement is the basis and the indicator showing the change in the spatial characteristics, as well as a measure and the reflector of the nature of social and economic processes in a particular area, in this case - Kazan County. The purpose of the article is to familiarize with the first attempts to implement spatial and statistical analysis of data on rural settlement in the middle of the XIX century using the methods of modern computer technology. Leading techniques to the study of this problem are the methods of spatial and spatial-statistical analysis of the data. To visualize the settlement structure of the Kazan district an electronic cartographic base was used, established on the basis of the map of 1910. The localization of the settlements accounted by the Ministry of Internal Affairs in 1859, and a spatial analysis of the current settlement system revealed the areas of the greatest concentration of the settlements, relatively uniform distribution of settlements on the existing three county units of the County and uneven distribution of different their types, socio-ethnic and religious heterogeneity of the population, the direction of the main vector of economic and economic development, the beginnings of the industrial production in the county. Study materials are the most time-consuming and defining step in the creation of the historical geographic information system (GIS).
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".