What Do We Learn from Good Practices of Biologically Inspired Design in Innovation?
Bibliographic record
Abstract
Biologically inspired design (BID) is an emerging field of research with increasing achievements in engineering for design and problem solving. Its economic, societal, and ecological impact is considered to be significant. However, the number of existing products and success stories is still limited when compared to the knowledge that is available from biology and BID research. This article describes success factors for BID solutions, from the design process to the commercialization process, based on case studies and market analyses of biologically inspired products. Furthermore, the paper presents aspects of an effective knowledge transfer from science to industrial application, based on interviews with industrial partners. The accessibility of the methodological approach has led to promising advances in BID in practice. The findings can be used to increase the number of success stories by providing key steps toward the implementation and commercialization of BID products, and to point out necessary fields of cooperative research.
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 | 0.033 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| 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".