An Engineering-to-Biology Thesaurus for Engineering Design
Why this work is in the frame
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Bibliographic record
Abstract
Engineering design is considered a creative field that involves many activities with the end goal of a new product that fulfills a purpose. Utilization of systematic methods or tools that aid in the design process is recognized as standard practice in industry and academia. The tools are used for a number of design activities (i.e., idea generation, concept generation, inspiration searches, functional modeling) and can span across engineering disciplines, the sciences (i.e., biology, chemistry) or a non-engineering domain (i.e., medicine), with an overall focus of encouraging creative engineering designs. Engineers, however, have struggled with utilizing the vast amount of biological information available from the natural world around them. Often it is because there is a knowledge gap or terminology is difficult, and the time needed to learn and understand the biology is not feasible. This paper presents an engineering-to-biology thesaurus, which we propose affords engineers, with limited biological background, a tool for leveraging nature’s ingenuity during many steps of the design process. Additionally, the tool could also increase the probability of designing biologically-inspired engineering solutions. Biological terms in the thesaurus are correlated to the engineering domain through pairing with a synonymous function or flow term of the Functional Basis lexicon, which supports functional modeling and abstract representation of any functioning system. The second version of the thesaurus presented in this paper represents an integration of three independent research efforts, which include research from Oregon State University, the University of Toronto, and the Indian Institute of Science, and their industrial partners. The overall approach for term integration and the final results are presented. Applications to the areas of design inspiration, comprehension of biological information, functional modeling, creative design and concept generation are discussed. An example of comprehension and functional modeling are presented.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it