BIKAS: Bio-Inspired Knowledge Acquisition and Simulacrum—A Knowledge Database to Support Multifunctional Design Concept Generation
Bibliographic record
Abstract
ABSTRACT A detailed acquisition, analysis, and representation of biological systems exhibiting different functions is required to develop unique bio-inspired multifunctional conceptual designs and methods. This paper presents BIKAS: Bio-inspired Knowledge Acquisition and Simulacrum, a knowledge database of biological systems exhibiting various functionalities, developed based on case-based bio-inspired examples from literature. The knowledge database represents the biological features, their characteristics, and the function exhibited by the biological feature as a combination of its integrated structure and structural strategy. Furthermore, this knowledge database is utilized by the Expandable Domain Integrated Design (xDID) model that works on classifying, mapping, and representing biological features into their respective geometric designations called Domains. The combination of features from the Domains results in the generation of multifunctional conceptual designs. In addition, Meta-level design factors are proposed to aid designers in filtering the biological features and their respective functions having a similar structural strategy, thus aiding designers in rapidly selecting and emulating biological functions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".