Sensing in nature: using biomimetics for design of sensors
Bibliographic record
Abstract
Purpose The purpose of this paper is to illustrate how biomimetics can be applied in sensor design. Biomimetics is an engineering discipline that uses nature as an inspiration source for generating ideas for how to solve engineering problems. The paper reviews a number of biomimetic studies of sense organs in animals and illustrates how a formal search method developed at University of Toronto can be applied to sensor design. Design/methodology/approach Using biomimetics involves a search for relevant cases, a proper analysis of the biological solutions, identification of design principles and design of the desired artefact. The present search method is based on formulation of relevant keywords and search for occurrences in a standard university biology textbook. Most often a simple formulation of keywords and a following search is not enough to generate a sufficient amount of useful ideas or the search gives too many results. This is handled by a more advanced search strategy where the search is either widened or it is focused further mainly using biological synonyms. Findings A major problem in biomimetic design is finding the relevant analogies to actual design tasks in nature. Research limitations/implications Biomimetics can be a challenge to engineers due to the terminology from another scientific discipline. Practical implications Using a formalised search method is a way of solving the problem of finding the relevant biological analogies. Originality/value The paper is of value as most present biomimetic research is focused on the understanding of biological phenomena and does not have as much focus on the engineering design challenges.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".