Behavior of natural organisms as a mimicking tool in architecture
Bibliographic record
Abstract
The relation in between architecture and nature has been one of combination for the last 400 years. Throughout history, architects have looked to nature for inspirations for building shapes, forms, and ornamentation without understanding nature's behavior. Moreover, new architectural approaches are being called for integrating nature as a tool for solving problems and enhancing adaptation within the context. This has been recently implemented in biomimicry theories that are applied in design processes. Biomimicry is considered a new discipline that studies living organisms' design and behavior in nature to solve human problems. Not only does this help in finding new ways for adaptation, it also generates new sources of inspiration for aesthetic expressions. This is of great importance nowadays as buildings are becoming inefficient; consuming a lot of energy, materials, and resources. Furthermore, construction processes are becoming increasingly unsustainable. While on the other hand organisms are creating effective and intelligent solutions in their homes by using less material. Engineers can also mimic natural methods of construction for building and design rather than their exact shapes. In addition, they also lead to efficiency in terms of energy, material usage, time, effort, and cost and can promote more adaptable, sustainable, and optimum solutions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".