An inductive method for classifying building form in a city with implications for orientation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The utilization of deep learning for form analysis facilitates the classification of an extensive number of forms based on their morphological features. A critical consideration for implementing such analysis methods in architectural or urban forms is whether building orientation should be embedded within the data. Orientation functions as a form variable significantly influenced by environmental, social, and cultural contexts within a city. In contrast to other domains where forms are extrapolated in relation to their context, in the city, domain orientation uniquely characterizes building form. In this paper, we introduce a pipeline for constructing an extensive building form dataset and scrutinizing the morphological identity of building forms, with a particular focus on the implications of building orientation as a manifestation of urban locality. Through a case study situated in Montreal, we engage in a comparative analysis employing two distinct datasets—those with orientation-embedded forms and those with orientation-normalized forms. Our research aims to investigate the typo-morphological characteristics of the building forms of the city and to examine how building orientation contributes to the identification of these traits and mirrors urban locality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it