Biomimetics and its tools
Bibliographic record
Abstract
Biomimetics, as the transfer of strategies from biology to technology, is an emerging research area and has led to significant concepts over the past decades. The development of such concepts is described by the process of biomimetics, encompassing several steps. In Practice, beneficiaries of the process face challenges. Therefore, to overcome challenges and to facilitate the steps, tools have been developed in various areas, such as engineering, computing and design. However, these tools are not widely used yet. This paper presents an overview and a classification study of more than 40 tools with qualitative criteria. The criteria included, for example, the year of development, the accessibility of tools, the facilitated steps of the process or their contribution to sustainability. The classification shows that certain steps of the process and their challenges are well addressed by the tools, while other steps are not. The presented results contribute to the proposal of an improvement of the state of the art, and they build the foundation for future theoretical and practical analyses. These findings could contribute to increasing the implementation of biomimetics in various disciplines in the long term.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".