Bibliographic record
Abstract
Parametric Landscapes looks to answer the question of how can parametric scripts be utilized in the field of Landscape Architecture to increase efficiency, and inform smarter design and planning decisions. This body of work was conducted using applied research methods, drawing from both Landscape Architecture, and computer programming. The following scripts are intended to be used and manipulated by not only computer programmers, but by Landscape Architects and any other designer that would benefit from their application in their design workflow. These scripts are written in Python programming language applied in both Grasshopper and Rhinoceros. Parametric Landscapes starts with preliminary research, investigating the application of parametric programming software in Landscape Architecture. The application of parametric scripts throughout the site analysis and design phase are then explored at the site of Pinawa Dam in southeastern Manitoba. The design of this project is dependent upon the development of dynamic parametric scripts that allow the designer to easily input design data (drawings, or 3D models), and get seemingly instant feedback. Each parametric script in Parametric Landscapes is dependent upon a 3D digital landscape model. For the test site of Pinawa Dam a 3D landscape model is constructed from a point cloud derived from a drone scan. A parametric script isolates vegetation and topographic data from the point cloud. This data is then used to map vegetation, and produce a 3D digital topographic model. This model is then used to simulate drainage on the site, plan trail systems at accessible slope percentages, and is the foundation for a real-time cut and fill script.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.014 |
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".