Bibliographic record
Abstract
Biomimetic design uses ideas from biological phenomena as inspiration in design. To support biomimetic design, biological analogies are identified by finding instances of functional keywords that describe the engineering problem in biological knowledge in natural-language format. Challenges in using this approach include the identification of keywords, and the quantity and quality of results found. WordNet, a lexical database, is used as a language framework to systematically generate alternative keywords to find matches and analyze the results of searches. Troponyms from WordNet were found to provide better and more plentiful keywords than did synonyms. Due to the potentially large number of matches to keywords, matches are analyzed to facilitate extraction of dominant biological phenomena associated with keywords. This analysis found that words that frequently collocated with keywords tend to be objects of the keyword verb or agents that carry out the actions of the keyword. Furthermore, nouns that are inanimate, e.g., substances, tend to be objects, and nouns that are animate e.g., animals, organs, tend to be agents. Distinguishing frequently collocated words and their relationships to keywords can be used to facilitate identification of biological analogies in natural-language format to support design.Copyright © 2004 by ASME
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".