Application of Computer Aided Design Technology in Landscape Design
Bibliographic record
Abstract
With the progress of human civilization, people's pursuit of beauty is increasing. The traditional landscape architecture design methods and layout can no longer meet the aesthetic needs of modern society. At the same time, with the increasing maturity of computer technology, the planning and design of landscape architecture is constantly intersecting with many related disciplines, which makes the integration of computer and landscape architecture planning and design more closely intertwined. This article points out the main application directions of computer-aided design (CAD) in landscape architecture and provides a construction plan of an ecological auxiliary system. CAD-based ecological environment analysis methods have been introduced in landscape planning, with meteorological simulation, wind environment simulation, light environment simulation, water environment simulation, and ecological environment simulation as research objects. In the efficiency data statistics of CAD in landscape architecture design, AutoCAD technology reduces the design cycle by 20% and improves design efficiency by 30%. Therefore, the narrow understanding of traditional landscape architecture design has been extended to computer-aided landscape architecture planning and design, which has important value for landscape architecture planning and design in China.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".