Bio-inspired Materials and Sensing Systems
Bibliographic record
Abstract
Can scientists and engineers replicate Nature and develop systems that operate in extreme environments? Bio-inspiration is an established concept which is developing to meet the needs of the many challenges we face particularly in defence and security. This book explores the potential of bio-inspired materials and sensing systems together with examples of how they are being implemented. It is not an exhaustive study of the subject but provides an overview of how bio-inspired or -derived approaches can be used to enhance components, systems and systems of systems for defence and security applications. Readers will gain an awareness of the complexity and versatility of bio-inspired components as well as an understanding of how these technologies can be applied in a variety of operational scenarios. Consideration is given to using a conceptual model that can be deployed in distributed or autonomous operations. Using this model, bio-inspiration with behavioural science plays a major role in identification, movement, searching strategies and pattern recognition for chemical and biological detection. Examples focus on both learning new things from nature that have application to the defence and security areas and adapting known discoveries for practical use by these communities. This graduate level monograph provides an increased awareness of the need for more sophisticated, networked sensors and systems in the defence and security communities and will be of interest to both specialists in this area and science and technology generalists.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".