Bibliographic record
Abstract
Nature is a source of knowledge and inspiration for sustainable innovative solutions. Through biomimetic design, nature solutions are studied, abstracted and transferred to technology and other domains of applications. Sustainability and ideality are basic notions in design. While ideal systems had always been aspired for, having sustainable systems is a relatively new demand. In this paper we explore the similarity and differences between these two basic notions and suggest that there is a strong relation between ideality and sustainability. Based on this relation we analysed biological systems by a particular ideality framework and identifi ed repeated ideality strategies and design principles in nature. Selected examples of ideality analyses are presented as well as the list of ideality strategies that repeat in nature and represent nature sustainability strategies. These ideality strategies enrich current knowledge of sustainability strategies in nature (the life principles) by new operative and descriptive strategies. Ideality strategies are derived from a technical view that might be more inherent and applicable for engineers, observing biological systems as if they were technical systems. Using the ideality framework and strategies as a sustainability tool to address sustainable biomimetic design processes is further discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".