Bridging Cross-Domain Terminology for Biomimetic Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work aims to improve creativity and innovation in design by facilitating the use of cross-domain analogies, particularly from biological phenomena, as stimulus for concept generation. Rather than create an enormous database of biological knowledge to specifically support engineering design, we have chosen to take advantage of the large amount of biological knowledge already in natural-language format, e.g., books, journals, etc. Relevant biological analogies for any given design problem are found by searching for instances of functional keywords that describe the intended effect of the design solution in a natural-language corpus. However, the optimal choice of keywords, or search terms, is complicated by the fact that engineers and biologists may use differing domain-specific lexicons to describe related concepts. Therefore, an engineer without sufficient background in biology may not be able to identify keywords with biological connotation that are not obviously related to the engineering keywords. This paper describes efforts to bridge the gap in lexicons by examining words that frequently collocate with searched words. The biological meaningfulness of these bridge words is characterized by how frequently they occur within definitions of biological terms in a biology dictionary. Search words identified this way may not be obvious to domain novices, and may parallel those suggested by domain experts, thus facilitating the use of cross-domain ideas to support design. Our approach of generating bridge words with biological meaningfulness is generic and can be used to bridge any disparate domains (e.g., engineering and economics). Thus designers are enabled to quickly access relevant concepts from different domains to produce more innovative solutions.
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.
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