10,000 iterations: Computation as a Tool for Schematic Design
Bibliographic record
Abstract
Design as an exploratory method is an iterative process cycling through analysis, proposal, evaluation, and refinement.The dominant way of communicating ideas through drawings and models is limited by the static nature of the media.As an alternative approach, how can computational methods be used as tools for assisting in preliminary design?10,000 Iterations studies computation as a supplementary tool for schematic design by developing an evolutionary model that generates optimized layouts according to the architect's criteria.This process, due to its computational nature, is limited to the aspects of design that can be expressed mathematically.A simple design brief is developed as a critical method for refining the effectiveness and feasibility of this tool.The layouts generated by this process give the architect function driven material to consider for further development early on in the design process.ABSTRACT // 10,000 ITERATIONS // iii I would like to first thank my family, for their unconditional support and encouragement in everything I do.Mom, thank you for checking up on me and making sure I was taking care of myself.Dad, thank you for the many long discussions on what must have seemed like the most random of topics.
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 | 0.004 | 0.016 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".