Supporting Biomimetic Design by Embedding Metadata in Natural-Language Corpora
Bibliographic record
Abstract
Biology is a good source of analogies for engineering design. One approach of retrieving biological analogies is to perform keyword searches on natural-language sources such as books, journals, etc. A challenge in retrieving information from natural-language sources is the potential requirement to process a large number of search results. This paper describes how inserting metadata such as part-of-speech, word sense and lexicographical data for each word in a natural-language source can help users identify relevant biological stimuli for biomimetic design. Although this research is still exploratory, initial qualitative observations demonstrate successful identification and separation of biological phenomena relevant to either desired functions or desired qualities. In addition, by incorporating the aforementioned metadata, we can automatically remove search results where search keywords act on abstract nouns or where keywords are used in irrelevant senses. The benefits of embedding metadata are demonstrated through a case study on the redesign of a fuel cell bipolar plate. In this case study, our method can be used to hide 64% of the search results that are unlikely to contain useful biological phenomena, reducing the effort to systematically identify relevant biological analogies.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".