The comparative numerical analysis of nature and architecture: A new framework
Bibliographic record
Abstract
Maintaining or creating a visual relationship between the form of a building and its surrounding natural landscape is often cited as a crucial factor in producing designs that support psychological comfort or environmental sustainability. While multiple methods for the analysis of nature and architecture have developed over time, only a handful of past studies have ever attempted to quantitatively compare the geometric properties of nature to those of architecture. Fractal analysis provides one of the very few methods available to analyse and compare the geometry of diverse objects. The fractal dimension (D) of an object is a numerical value which reflects the volume and distribution of detail in an item. Of the many subjects analysed using this method, the forms of nature (such as coastlines, rivers and plant elements) have been successfully measured, as have built forms (such as houses, public buildings and cityscapes). However, despite the method's application to each subject area, few examples exist where fractal dimension data derived from nature are compared with equivalent architectural data. A primary reason cited for this situation is the disparity of methodological variables, in particular, representational approaches to the images used for comparison are presently disparate and uncategorised.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".