Factors Affecting Late Twentieth Century Land Use Patterns in Kamakura City, Japan
Bibliographic record
Abstract
Abstract Land use patterns can be viewed as a reflection of various factors including zoning regulations and environmental and social influences. To understand the effects of these factors, late twentieth century land use data for Kamakura City, Japan were analysed in relation to zoning regulations and geomorphological influences using geographic information systems techniques. Kamakura is a typical example of an Asian historic city experiencing the pressures of recent urbanisation. Statistical analyses, including principal component analysis, were employed to identify factors affecting changes in major land use patterns over time and space. The rapid increase in population and government regulation in the 1970s led to the construction of low‐rise buildings in piedmont areas. However, topography and conservation activities limited construction to areas with slope angles below about 10°. In the late 1980s and early 1990s, the area of vacant land on the urban lowlands increased due to the large increases in land value during the Japanese Bubble Economy phase. Land use intensified in most urban areas, although hilly areas were still characterised by low‐rise buildings. Although urban sprawl has occurred in many cities in Asia, this has been limited in Kamakura because of strong land use regulation and conservation activities. However, the city has experienced high‐rise development in its urban lowlands.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".