Regional Plans of Mountainous City Architecture Based on Ecological System
Bibliographic record
Abstract
On the basis of the research data of 120 enterprises from 10 development zones in Gansu Province, we conducted regional planning research combining enterprise architecture with ecological green space environment system. We determined that the largest part of the green space pattern is the “other green space,” which mainly depends on the formation of super large plantation. However, this part is consistently decreasing in recent years. Affiliated green space became the largest in the green space patterns because of the high degree of vertical greening of the enterprise architecture in the development zones. The high fragmentation of the affiliated green space also leads to the high fragmentation of the entire green space of the development zone. In future regional planning of development zones, future planning of the green space can take the current green space as the basis, use the road green space and green corridors nearby waters as the basic framework to connect other patterns of green space, and form a ring-shaped enclosure, reticular structure, and wedge-shaped and dotted supplemented regional pattern. Meanwhile, the road green space system can be reasonably arranged by setting new green space spots at road intersections and key strategic positions to connect the isolated green space patches and improve the connectivity of the green space.
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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 | 0.003 | 0.004 |
| 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) | 0.004 | 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".