Urban Systems: How Can a Parametric Building Information Model Serve to Democratize, Optimize and Challenge the Master Planning Process at Carleton University?
Bibliographic record
Abstract
Carleton University Campus is an example of an isolated institution with a masterplan to direct the growth of undeveloped space -the currently used 2010 masterplan is a projected snapshot of fifty years for that space.This thesis project explores the visualization of real-time datasets and how it might redefine the process of designing built assets for Carleton University campus.A parametric masterplan of the campus would present a flexible alternative to the current static ideal re-planned every five years.Proposed is a parametric visualization of datasets from which challenging decisions may be guided by accurate digital representations of real-world information.Presented are techniques in parametric scripting to create a Building Information Model (BIM) of the Carleton University campus embedded with information on infrastructure, people and energy consumption.A parametric BIM of Carleton University campus can exist as a detailed resource of information and statistics that parallel the operation and development of its physical counterpart.The project explores the potential for parametric virtual representations of datasets to narrow the distance between the digital and the material world.It questions the implications of built assets on campus, results of which can be used as input parameters to create a feedback loop that further improves the datasets.Furthermore, the parametric masterplan can be used to compare simulated results with proposed real-world scenarios to help predict positive or negative impacts on Carleton campus.The goal of this parametric visualization is to democratize a variety of real-time datasets to the community at large so that they may establish quality proposals for the campus.iii
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".